Melanoma diagnostic system based on neural convolutional networks
Patent Information
- Application Number
- DE202024105498
- Authority / Receiving Office
- DE · DE
- Patent Type
- Utility models
- Current Assignee / Owner
- Filing Date
- 2024-09-24
- Publication Date
- 2025-03-06
- Estimated Expiration
- 2034-09-30
Smart Images
Figure 00000000_0000_ABST
Abstract
Description
Field of the invention:
[0001] The present invention relates to the field of medical diagnostics and, more particularly, to a system that uses convolutional neural networks (CNNs) for the automatic detection and diagnosis of melanoma, a severe form of skin cancer. Background of the invention:
[0002] Melanoma is one of the deadliest forms of skin cancer, and early detection is crucial for effective treatment. Traditional diagnostic methods rely heavily on the experience and skill of dermatologists, leading to variable diagnoses and potential delays. Advances in medical imaging and artificial intelligence, particularly convolutional neural networks, offer the potential to automate melanoma detection and improve its accuracy. CNNs are capable of analyzing complex patterns in medical images and have shown great promise in detecting various diseases. However, existing systems are often limited in real-time performance, accuracy, and integration into clinical workflows.This invention attempts to address these challenges by providing a high-accuracy CNN-based melanoma diagnosis system to be used as a decision support tool for healthcare professionals.
[0003] Melanoma, a serious form of skin cancer, is a major concern for healthcare professionals due to its high mortality rate and the urgent need for early diagnosis. Traditional methods for diagnosing melanoma rely primarily on visual examinations by dermatologists using dermoscopic instruments, followed by biopsies for pathological analysis if necessary. These techniques are often subjective and highly dependent on the skill and experience of the dermatologist, leading to potential inconsistencies in diagnosis. Dermatologists may miss early-stage melanomas due to the subtlety of visual symptoms or mistakenly classify benign lesions as malignant, leading to unnecessary biopsies and patient anxiety.The accuracy of these conventional diagnostic methods is reported to be between 60 and 80%, a range that highlights the need for improvement in accuracy, precision, and repeatability.
[0004] In response to these challenges, computer- and image-based solutions have emerged, leveraging advances in machine learning and artificial intelligence. One of the earliest approaches involved the use of digital image analysis systems that employed traditional machine learning techniques such as support vector machines (SVM), K-nearest neighbors (KNN), and decision trees to classify skin lesions. These methods used manually created features based on visual properties such as color, texture, and shape extracted from images of skin lesions. While these approaches showed moderate success in improving diagnostic accuracy, they were limited by their reliance on handcrafted features, which often did not capture the complex details required for reliable melanoma detection.These systems also required extensive preprocessing and feature engineering, making their implementation more complex and limiting their applicability in real-time clinical environments.
[0005] Another early wave of melanoma diagnostic systems used image segmentation techniques that extracted lesion boundaries from dermoscopic images to analyze lesion symmetry, edge irregularities, and color variation—features typically associated with melanoma. Image segmentation methods such as thresholding, region growing, and edge detection were applied to dermoscopic images to enable more detailed analysis of lesion characteristics. However, these methods were not without drawbacks. For example, the segmentation techniques struggled with variations in lighting conditions, skin tones, and lesion sizes, leading to inconsistent results. Furthermore, segmenting complex or irregularly shaped lesions was challenging and often resulted in false positive or false negative results.The need for precise segmentation also introduced a level of complexity that made real-time implementation in clinical settings difficult.
[0006] With the advancement of deep learning, particularly convolutional neural networks (CNNs), new frontiers in melanoma detection emerged. CNNs are extremely effective at image classification and were considered a promising solution for skin cancer diagnosis due to their ability to automatically learn and extract relevant features from images without the need for manual feature engineering. Early implementations of CNNs for melanoma diagnosis leveraged pre-trained models such as VGGNet, AlexNet, and ResNet, which had demonstrated strong performance in general image classification tasks. These networks were optimized on datasets of dermoscopic images to classify skin lesions as benign or malignant. CNNs offered significantly improved accuracy compared to traditional machine learning techniques, achieving classification accuracy rates exceeding 85–90% in many cases.
[0007] Despite the promising approaches of CNNs, existing solutions still suffer from several drawbacks. One major problem is that large, annotated datasets are required to effectively train the models. While CNNs can automatically learn features from images, they require a significant amount of labeled data to generalize well between different types and conditions of skin lesions. In medical imaging, acquiring large, high-quality datasets is often challenging due to concerns about patient privacy, the logistics of data collection, and the relatively low incidence of melanoma compared to other skin diseases. As a result, many CNN-based melanoma diagnosis systems are prone to overfitting, where the model achieves good results based on the training data but fails to generalize to unknown cases.This limits their clinical applicability and raises concerns about the reliability of such systems when used in real-world diagnostic scenarios.
[0008] Another limitation of existing CNN-based systems is their sensitivity to variations in image quality and conditions. Dermoscopic images can vary considerably in illumination, resolution, and the presence of artifacts such as hair or skin reflections. Although CNNs are capable of learning robust features, they are still prone to misclassification when presented with low-quality or noisy images. This has led to the development of various image preprocessing techniques aimed at improving image quality before feeding them into the CNN. However, these preprocessing steps can be computationally intensive and lead to delays in real-time diagnostic applications.
[0009] Furthermore, existing CNN-based melanoma diagnosis systems often lack interpretability, which poses a major challenge for their adoption in clinical settings. Although CNNs are highly accurate, they function like "black boxes," offering little insight into the reasons behind their predictions. In a medical context, it is critical for healthcare providers to understand the reasons behind a system's diagnosis, especially for life-threatening conditions such as melanoma. Without a clear understanding of how the system reached its conclusion, clinicians may be reluctant to fully trust the system, potentially limiting its usefulness as a decision-making aid. Efforts to improve interpretability have been introduced, such asSalience maps and attention mechanisms, but these methods are still in their infancy and often do not provide the level of transparency required in medical diagnostics.
[0010] Another disadvantage of existing solutions is the lack of integration into clinical workflows. Many CNN-based systems are standalone tools that require dermatologists to manually enter images and retrieve results, introducing additional steps in the diagnostic process. For a system to be widely adopted, it must integrate seamlessly with existing electronic health record (EHR) systems, allowing for easy data transfer, retrieval, and analysis without interrupting the physician's workflow. The lack of such integration has hampered the adoption of many promising melanoma diagnostic systems into clinical practice.
[0011] Furthermore, the majority of CNN-based melanoma diagnostic systems focus exclusively on classification tasks, ignoring the broader diagnostic process. For example, these systems often provide a binary output—benign or malignant—without providing additional diagnostic insights such as the lesion's specific characteristics or risk factors. Dermatologists rely on more than just a binary classification when diagnosing melanoma; they consider several factors, including the lesion's size, growth rate, and the patient's medical history. By focusing only on classification, existing systems miss the opportunity to provide a more comprehensive diagnostic tool that could help dermatologists make more informed decisions.
[0012] Finally, existing solutions often overlook the potential for continuous learning and improvement. As new cases of melanoma are diagnosed and treated, the knowledge base of what constitutes a malignant lesion evolves. However, many CNN-based systems are static, trained once with a fixed dataset and deployed without the ability to learn from new data. This limits their long-term effectiveness, as they cannot adapt to the evolving nature of melanoma presentation. Incorporating continuous learning mechanisms, where the system can update its knowledge base with new data, could significantly improve the diagnostic accuracy and relevance of these systems over time. Summary of the invention:
[0013] The present invention is a CNN-based melanoma diagnosis system that uses a multi-stage deep learning architecture to process images of skin lesions and classify them as benign or malignant melanoma. The system is designed to integrate seamlessly into clinical settings and provide real-time diagnostic assistance. The system comprises several modules, including an image acquisition unit, a preprocessing unit, a feature extraction unit, a classification unit, and a result interpretation module. The image acquisition unit acquires high-resolution images of skin lesions, while the preprocessing unit enhances the images by removing noise and adjusting contrast. The feature extraction unit, powered by a convolutional neural network, identifies and processes relevant features from the images.The classification unit then uses these features to determine whether the lesion is benign or malignant. The system also includes a results interpretation module that generates detailed reports and supports medical professionals in decision-making. Furthermore, the invention includes a device structure that supports the diagnostic process and includes hardware elements designed to facilitate image acquisition, processing, and data communication.
[0014] The primary objective of the present invention is to provide a convolutional neural network (CNN)-based system for the accurate and efficient diagnosis of melanoma, a severe form of skin cancer, through automated image analysis of skin lesions. The invention aims to overcome the limitations of traditional diagnostic methods by providing a real-time, AI-based tool that improves the accuracy of detecting malignant melanomas. One of the specific objectives of this invention is to eliminate the need for manual feature extraction by utilizing a deep learning architecture capable of identifying complicated patterns and features in dermoscopic images. This will help minimize the risk of human error and ensure consistency in diagnoses.
[0015] Another goal of the invention is to improve existing CNN-based systems by addressing the issues of data quality and image variability. The system is designed to integrate preprocessing techniques capable of handling images with different resolutions, lighting conditions, and the presence of artifacts to ensure the robustness of the diagnostic process in diverse clinical settings. Furthermore, the invention aims to provide a system capable of processing dermoscopic images in real time, allowing medical professionals to obtain immediate results during patient consultations.
[0016] The invention also aims to improve the interpretability of CNN models in medical diagnostics by integrating tools and mechanisms that explain the basis of the diagnosis. This provides clinicians with a deeper understanding of how the system reached its conclusion, which in turn increases confidence and facilitates adoption into clinical practice. Furthermore, the invention aims to offer a modular design that enables seamless integration into existing clinical workflows and electronic health record (EHR) systems, ensuring that the system can be easily implemented in hospitals and dermatology clinics without disrupting standard procedures.
[0017] Finally, the invention aims to provide a scalable and continuously improving system that can evolve with the availability of new data. By incorporating continuous learning mechanisms, the system can update its knowledge base and improve its diagnostic accuracy over time. This ensures that the system remains relevant and accurate in the long term, adapting to new melanoma cases and advances in dermatological research. Through these innovations, the invention is intended to significantly improve the early detection of melanoma and ultimately contribute to better patient outcomes and a reduction in melanoma-related mortality rates. SHORT DESCRIPTION OF THE FIGURE
[0018] These and other features, aspects, and advantages of the present invention will become more readily understood when the following detailed description is read in conjunction with the accompanying drawings, in which like characters represent like parts throughout. Fig. Figure 1 shows a block diagram of a melanoma diagnosis system based on a convolutional neural network.
[0019] Furthermore, those skilled in the art will appreciate that elements in the drawings are shown for convenience and may not necessarily be drawn to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawing may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art who would benefit from the description herein. Detailed description:
[0020] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation on the scope of the invention is intended thereby, since such changes and further modifications of the illustrated system, and such further applications of the principles of the invention as illustrated therein, are contemplated as would normally occur to one skilled in the art to which the invention pertains.
[0021] It will be understood by one skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.
[0022] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the phrase "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not all refer to the same embodiment.
[0023] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps not only includes those steps, but may also include other steps not expressly listed or inherent in such process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" does not preclude, without further limitation, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.
[0024] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.
[0025] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
[0026] Fig. Figure 1 shows a block diagram of a convolutional neural network-based melanoma diagnostic system comprising: an image acquisition unit 102 configured to capture high-resolution dermoscopic images of skin lesions, the image acquisition unit comprising a dermoscopic camera integrated into a portable or fixed device; an image preprocessing unit 104 operatively connected to the image acquisition unit, the image preprocessing unit comprising a hardware-based processing module for enhancing the captured images by reducing noise, normalizing contrast, and removing artifacts;a feature extraction unit 106 operatively connected to the image preprocessing unit, wherein the feature extraction unit comprises a convolutional neural network configured to process the enhanced images by analyzing skin lesion features, including texture, color variance, and shape, and to automatically extract relevant diagnostic features; a classification unit 108 operatively connected to the feature extraction unit, wherein the classification unit comprises a GPU-based processing module for classifying the skin lesion as benign or malignant based on the extracted features and generating a classification output along with a confidence value;a result interpretation unit 110 operatively connected to the classification unit, the result interpretation unit comprising a display module and a data storage module for generating and storing a diagnostic report including the classification output, the confidence score, and the enhanced image with marked regions of interest; and a data communication unit 112 operatively connected to the result interpretation unit, the data communication unit comprising a wireless transceiver configured to transmit the diagnostic report to an external database or electronic health record (EHR) system.
[0027] In one embodiment, the image pre-processing unit 104 further comprises a lighting correction module configured to adapt to variable lighting conditions during image acquisition, thus ensuring consistent image quality in different environments.
[0028] In one embodiment, the feature extraction unit 106 further comprises multiple convolutional layers followed by max-pooling layers and activation functions, wherein the convolutional layers are configured to detect low-level and high-level patterns within the dermoscopic images, thereby enabling robust detection of melanoma features such as asymmetry and irregular edges.
[0029] In one embodiment, the classification unit 108 further comprises a hardware-based confidence assessment module configured to provide a quantitative score representing the probability that the lesion is malignant based on the classification output generated by the convolutional neural network.
[0030] In one embodiment, the result interpretation unit 110 further comprises an annotation module configured to place visual markers on the enhanced image and highlight the areas of the skin lesion that most significantly contributed to the classification decision of the convolutional neural network.
[0031] In one embodiment, the data communication unit 112 further includes an encryption module configured to encrypt the transmitted diagnostic report and patient data, thereby ensuring secure communication with external medical databases and compliance with data protection regulations.
[0032] In one embodiment, the data communication unit 112 further includes a cloud-based server interface module that enables real-time updates of the convolutional neural network model by retrieving new training data from a remote server and regularly updating the classification accuracy of the system.
[0033] The CNN-based melanoma diagnosis system operates with a series of interconnected hardware and software components that work together to enable accurate and real-time diagnosis of melanoma. The image acquisition unit is the first component of the system and consists of a high-resolution camera capable of capturing dermoscopic images of skin lesions. This camera is integrated into a portable or fixed device that ensures consistent image quality regardless of ambient light conditions. The camera communicates with the preprocessing unit, which consists of a dedicated image enhancement processing module. This preprocessing module uses advanced image processing techniques to reduce noise, normalize contrast, and correct lighting inconsistencies, ensuring that the images are suitable for analysis by the neural network.
[0034] The classification unit is responsible for determining whether a lesion is malignant or benign. It uses the extracted features to make a final classification based on the patterns identified by the CNN. This unit operates in real time and is designed for high accuracy, with a precision rate significantly higher than traditional diagnostic methods. The system provides the user with feedback in the form of a confidence score indicating the probability that the lesion is a melanoma. The classification results are then sent to the result interpretation module.
[0035] The results interpretation module generates a detailed diagnostic report that can be reviewed by medical professionals. This report includes visual representations of the processed images, highlighting problem areas, and a detailed explanation of the CNN results. The system can be integrated into existing medical software platforms or operated as a standalone tool. The system's modular design allows for easy updates to the CNN model, enabling continuous improvements in diagnostic accuracy as new data becomes available.
[0036] The device designed to support the CNN-based melanoma diagnostic system consists of a portable or fixed imaging device that can be integrated into the rest of the system. The imaging device is equipped with a high-resolution dermoscopic camera capable of capturing images with sufficient resolution for diagnostic purposes. In the fixed version, this camera is mounted on an adjustable arm, allowing the user to precisely position it over the lesion. In the portable version, the camera is housed in an ergonomically designed housing that allows medical professionals to operate it comfortably and stably.
[0037] The processing hardware for the system is housed in a compact unit connected to the imaging device. This unit contains a dedicated graphics processing unit (GPU) that handles the computational demands of the CNN and ensures real-time image analysis. The system is designed to connect wirelessly to medical databases and patient data systems, enabling seamless integration into clinical workflows. The device is also equipped with a screen that provides the user with real-time feedback, including classification results and any additional diagnostic insights provided by the system.
[0038] The system's modular design allows for easy upgrades and maintenance. The image acquisition, processing, and classification units are connected via a high-speed data bus, ensuring minimal latency between image acquisition and diagnosis. The handheld version is powered by an internal battery, while the stationary version offers options for AC power or battery backup. Furthermore, the system supports secure cloud-based storage, ensuring that all diagnostic data is securely archived and can be accessed remotely by authorized medical personnel.
[0039] The invention represents a significant advance in the field of melanoma diagnosis, providing healthcare professionals with a reliable, AI-powered tool that improves both the speed and accuracy of skin cancer detection.
[0040] The melanoma diagnosis system, based on a convolutional neural network (CNN), operates with a carefully designed workflow that integrates hardware and software components to ensure accurate and efficient diagnosis of skin lesions, especially melanoma. The system begins with the image acquisition unit, which consists of a high-resolution dermoscopic camera integrated into a portable or fixed device. This camera is specifically designed to capture detailed images of skin lesions, providing the resolution and clarity required for an accurate diagnosis. The device ensures consistent image quality through its design, which adapts to varying lighting conditions and patient skin tones. Once the image is acquired, it is immediately transferred to the preprocessing unit.
[0041] The image preprocessing unit plays a crucial role in preparing the acquired image for analysis by the CNN. The preprocessing unit is equipped with a hardware-based processing module that performs several important tasks. First, the module reduces noise in the image, which can be caused by environmental factors or inherent imperfections in the image acquisition process. After noise reduction, the module normalizes the image contrast, ensuring that important visual features such as lesion edges and color variations are clearly distinguishable. Furthermore, the module can remove artifacts such as hair or reflections that could interfere with subsequent analysis, thus ensuring that the image is optimized for further processing.
[0042] After preprocessing, the image is passed to the feature extraction unit, where the core of the system—the convolutional neural network—comes into action. The CNN is designed to automatically analyze the preprocessed images and extract relevant diagnostic features. The CNN architecture comprises multiple layers of convolutional operations, followed by pooling and activation layers. These layers gradually detect low- and high-level features within the image. The initial layers detect basic patterns such as edges and simple shapes. As the image progresses through deeper layers of the CNN, more complex patterns are identified, such as the asymmetry, irregular borders, and color variations characteristic of melanomas.
[0043] Convolutional layers use learned filters to scan the image and capture these key diagnostic features. Subsequent pooling layers reduce the dimensionality of the image data, allowing the CNN to focus on the most prominent features while maintaining computational power. Activation functions such as Rectified Linear Units (ReLU) are applied to introduce nonlinearity into the model, allowing the CNN to better model complex relationships within the image data. The CNN's deep architecture enables the system to learn and recognize intricate patterns in skin lesions and to distinguish between benign and malignant cases with high accuracy.
[0044] Once the CNN has processed the image and extracted relevant features, the data is transferred to the classification unit. This unit relies on a GPU-based processing module, which ensures that the CNN's calculations can be performed in real time, enabling rapid diagnostic results. The classification unit analyzes the extracted features and assigns the skin lesion to one of two categories: benign or malignant. Classification is based on the patterns identified by the CNN, with the GPU providing the necessary processing power to perform this analysis quickly. Along with the classification, the unit generates a confidence score that indicates the probability that the lesion is malignant. This confidence score is crucial in giving the clinician a sense of how confident the system is in its diagnosis and provides a quantitative measure of reliability.
[0045] The classification results are then processed by the results interpretation unit, which is responsible for presenting the diagnostic information to the clinician. This unit generates a comprehensive diagnostic report that includes the original dermoscopic image, the enhanced and processed image, the classification result, and the confidence score. The report also highlights the areas of the lesion that were most relevant to the CNN's decision and uses visual markers to indicate areas of interest, such as irregular borders or unusual color distributions. This feature improves the interpretability of the system and provides the clinician with a clear understanding of the basis for the system's diagnosis.
[0046] The system is also equipped with a data communication unit that securely transmits the diagnostic report to external databases or electronic health record (EHR) systems. This unit includes a wireless transceiver that facilitates data transmission and an encryption module that ensures the privacy and security of patient information. The encrypted data can be sent to central medical databases for archiving purposes or shared with other healthcare providers, ensuring seamless integration into existing clinical workflows.
[0047] A key aspect of the system is its ability to continuously learn and improve. The continuous learning unit is designed to update the CNN model over time as new data becomes available. This unit is connected to a cloud-based server that stores new training data, including additional images of skin lesions. Periodically, the system retrieves this data and integrates it with the existing training set, allowing the CNN to be retrained and improve its diagnostic accuracy. The learning process involves fine-tuning the CNN's weights and filters to account for new patterns in the data, ensuring that the system remains current and accurate as medical knowledge evolves and new cases of melanoma are diagnosed.
[0048] The technique that guides the CNN's learning and diagnosis processes is based on supervised learning, in which the CNN is trained on a labeled dataset of dermoscopic images. During the training phase, the CNN learns to associate certain image features with the corresponding diagnosis and adjusts its internal parameters to minimize classification errors. This training process includes backpropagation, in which the errors made by the CNN during the classification task are propagated back through the network, allowing the system to adjust the weights of the filters in the convolutional layers. Over time, the CNN learns to accurately classify skin lesions by identifying subtle patterns indicative of melanoma. The continuous learning unit ensures that this learning process continues as new data is added, maintaining the system's high diagnostic performance.
[0049] The present invention is in the field of medical diagnostic systems, specifically in the field of automated image analysis and artificial intelligence for skin cancer detection. It involves the use of convolutional neural networks (CNNs) in combination with dermoscopic imaging and real-time data processing for the detection of melanoma. The invention comprises hardware and software components for acquiring, processing, and classifying dermoscopic images and provides a robust, AI-driven solution for improving the accuracy and efficiency of melanoma diagnosis. This system is designed for integration into clinical workflows and provides dermatologists with a reliable, automated decision support tool for diagnosing skin cancer and improving patient outcomes. Skin cancer is one of the most prevalent health problems in the human population.In recent years, the incidence of this disease has increased significantly, and it is one of the leading causes of death in both sexes. The disease manifests itself through the uncontrolled growth of melanocytes, the skin cells that produce the pigment melanin.
[0050] The factors that trigger mutations in melanocytes that lead to skin cancer remain unclear. However, the prevailing consensus emphasizes the role of ultraviolet (UV) radiation—both natural from the sun and artificial from devices such as tanning lamps—in increasing the risk of mutation. A staggering 86 percent of melanoma cases are attributed to UV radiation from the sun. Individuals with lighter skin, in particular, are more susceptible to melanoma. Melanoma can occur in various areas of the body, including the face, limbs, and back. Interestingly, it is not limited to sun-exposed regions; melanoma can also develop in places such as the palms, soles, and under the nails. Such hidden melanomas are more common in individuals with darker skin.It is noteworthy that approximately 30 percent of melanomas develop from pre-existing moles and have the potential to rapidly multiply and spread. When classifying skin tumors, they are primarily divided into two categories: benign (non-cancerous) and malignant (cancerous), as described in [Therapeutic Uses]. Fig. Melanoma, which is classified as a malignant tumor, often bears an uncanny resemblance to normal moles, which is why it is often misdiagnosed. It can also disguise itself as other harmless skin conditions such as age spots or cysts, making its identification difficult. Early detection is essential; if melanoma is not diagnosed in its early stages, it can develop severely and be life-threatening. A conventional mole is usually characterized by a uniform color such as light brown or brown, is clearly demarcated from the surrounding skin, and is generally oval or round with a diameter of less than 6 mm. Any deviation from these characteristics may indicate the development of melanoma. To facilitate the early detection of potential melanomas on the skin, the American Academy of Dermatology has introduced the mnemonic "ABCDE."
[0051] Without computer assistance, diagnostic accuracy for melanoma ranges between 65 and 80%. However, by incorporating dermoscopic images, this accuracy can be increased by approximately 49%. The challenge in accurately diagnosing melanoma often lies in its deceptive similarity to common skin diseases. This research leverages the potential of medical image-based diagnostic systems, focusing on advanced deep learning techniques such as convolutional neural networks (CNN) to enhance the discrimination between melanoma and benign skin cancer lesions. However, the complexity of these techniques can lead to the "black box" dilemma, which can undermine trust in artificial intelligence. The concept of Explainable AI (XAI) and tools such as LIME aim to bridge this gap by providing insights into machine learning predictions, thus ensuring transparency.Although deep learning models are suitable for tasks such as language and image processing, their credibility is occasionally overshadowed by their inherent lack of transparency and interpretability.
[0052] Humans cannot trust them because they do not justify their judgments or predictions. However, errors in artificial intelligence systems can be catastrophic depending on the application. Numerous strategies have been proposed to address these problems. To achieve this, the machine learning community has made explainable artificial intelligence (XAI) a popular research area. These approaches aim to explain deep machine learning models in a way that is simple enough for humans to understand. As described in Fig. As shown, the green-colored superpixels have a high probability that the image belongs to a particular category, while the red-colored superpixels have a lower probability that the image belongs to a category.
[0053] The inability of artificial intelligence (AI) models to clearly explain the reasons behind their decisions or predictions often undermines people's trust in them. The consequences of such a lack of transparency can be particularly serious, as errors made by AI systems in certain applications can have catastrophic consequences. In response to these challenges, countless solutions have been proposed. The machine learning community, recognizing the need for transparent AI, has increasingly turned to explainable artificial intelligence (XAI) as a central research area. The main goal of these XAI methods is to explain machine and deep learning models in a way that is intuitive and understandable to humans. As discussed in Fig.As shown, the green-shaded superpixels indicate a higher probability that the image will be assigned a specific label, while the red-shaded superpixels indicate a lower probability of such categorization. Despite the impressive performance of AI systems, however, they are often met with skepticism and concern due to their opacity and the potential for catastrophic errors. To address this problem, XAI has emerged as an important research area. XAI aims to simplify complex machine learning models and make them more understandable to humans. By improving the interpretability of AI systems, we can better understand the decision-making process of the techniques.
[0054] Explainable AI (XAI) facilitates interpretability by employing a range of methods that include visual, textual, and mathematical explanations. A widely used technique is the use of salience maps. These maps highlight the crucial elements within an input that significantly influence the model's conclusions. Such elements often manifest as heat maps or probabilistic outputs and provide visual insight into the model's decision-making trajectory. By carefully analyzing these explanations, a clearer understanding of the model's decision-making basis and its internal mechanisms can be achieved.
[0055] The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Furthermore, the actions of any flowchart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.
[0056] Advantages, other benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, benefits, solutions to problems, and any components that may cause an advantage or solution to occur or become more apparent are not to be construed as a critical, required, or essential feature or component of any or all of the claims. REFERENCES 100 Block diagram of a melanoma diagnostic system based on a convolutional neural network. 102 An image acquisition unit 104 An image preprocessing unit 106 A feature extraction unit 108 A classification unit 110 A result interpretation unit 112 A data communication unit
Claims
[1] A convolutional neural network-based melanoma diagnostic system comprising: an image acquisition unit configured to capture high-resolution dermoscopic images of skin lesions, the image acquisition unit comprising a dermoscopic camera integrated into a portable or fixed device; an image pre-processing unit operatively connected to the image acquisition unit, the image pre-processing unit comprising a hardware-based processing module for enhancing the acquired images by reducing noise, normalizing contrast, and removing artifacts; a feature extraction unit operatively connected to the image pre-processing unit, the feature extraction unit comprising a convolutional neural network configured to process the enhanced images by analyzing features of skin lesions, including texture, color variations, and shape, and automatically extracting relevant diagnostic features; a classification unit operatively connected to the feature extraction unit, the classification unit comprising a GPU-based processing module for classifying the skin lesion as benign or malignant based on the extracted features and generating a classification output along with a confidence value; a result interpretation unit operatively connected to the classification unit and comprising a display module and a data storage module for generating and storing a diagnostic report comprising the classification output, the confidence value, and the enhanced image with marked regions of interest; and a data communication unit operatively connected to the result interpretation unit, the data communication unit comprising a wireless transceiver configured to transmit the diagnostic report to an external database or electronic health record (EHR) system. [2] The system of claim 1, wherein the image pre-processing unit further comprises a lighting correction module configured to adapt to variable lighting conditions during image acquisition, thus ensuring consistent image quality in different environments. [3] The system of claim 1, wherein the feature extraction unit further comprises a plurality of convolutional layers followed by max-pooling layers and activation functions, the convolutional layers configured to detect low-level and high-level patterns within the dermoscopic images. [4] The system of claim 1, wherein the classification unit further comprises a hardware-based confidence evaluation module configured to provide a quantitative score representing the probability that the lesion is malignant based on the classification output generated by the convolutional neural network. [5] The system of claim 1, wherein the result interpretation unit further comprises an annotation module configured to place visual markers on the enhanced image and highlight the areas of the skin lesion that most significantly contributed to the classification decision of the convolutional neural network. [6] The system of claim 1, wherein the data communication unit further comprises an encryption module configured to encrypt the transmitted diagnostic report and patient data, thereby ensuring secure communication with external medical databases and compliance with data protection regulations. [7] The system of claim 1, wherein the data communication unit further comprises a cloud-based server interface module that enables real-time updates of the convolutional neural network model by retrieving new training data from a remote server and regularly updating the classification accuracy of the system.