CNN Skin Analysis with Auxiliary Classification and Heatmap Visualization
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Solution Overview
Problem
Current methods for assessing acne severity, such as the Global Acne Severity Scale, are time-consuming, labor-intensive, and prone to human error, and existing computer-aided techniques often require high-standard medical images or specific image acquisition protocols, limiting their applicability and accuracy.
Innovation Solution
A deep learning-based system using a convolutional neural network (CNN) trained for regression tasks with integer-only labels, incorporating auxiliary classification tasks like gender and ethnicity predictions, processes images from mobile devices to provide accurate acne severity analysis and visualization through heatmaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard acne assessment systems (Global Acne Severity Scale) are used, then acne severity can be assessed, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical assessment (human dermatologists visually evaluating acne) with an automated deep learning system using convolutional neural networks (CNNs). The system processes images through multiple CNN models that perform regression tasks to predict acne severity scores, eliminating the need for time-consuming human evaluation while maintaining or improving assessment accuracy.
Solution Approach 2:
The system enables self-assessment functionality where users can upload their own images and receive automated acne severity evaluations without requiring professional dermatologist intervention. The deep learning models automatically process the images and generate severity scores, allowing users to perform assessments independently and efficiently.
2Extent of automation
If existing computer-aided techniques are used, then automation is achieved, but high-standard medical images or specific image acquisition protocols are required
Solution Approach 1:
The patent transforms the requirements for image input by changing the parameters of image quality acceptance. Instead of requiring high-resolution medical-grade images with specific acquisition protocols, the system is designed to process lower-resolution images from mobile devices. The CNN architectures are trained and optimized to extract meaningful features even from limited-quality images, fundamentally changing the parameter thresholds for acceptable input images.
Solution Approach 2:
The system achieves universality by being able to process images from multiple sources (mobile devices, cameras, existing databases) without requiring source-specific processing pipelines. The deep learning models are designed to handle variations in image quality, resolution, and format, making the system adaptable to diverse image inputs while maintaining consistent automated assessment functionality across all sources.
3Measurement precision
If deep learning models are trained for regression tasks with integer-only labels, then accurate acne grading is achieved, but auxiliary information (gender, ethnicity) is not utilized
Solution Approach 1:
The patent merges multiple functions into a single integrated deep learning system. The CNN architecture simultaneously performs acne severity regression and auxiliary classification tasks (gender prediction, ethnicity prediction). By combining these functions in one model, the system utilizes all available information from the images, including demographic features, to improve overall assessment accuracy while eliminating the need for separate processing pipelines.
Solution Approach 2:
The system expands the problem from a single-dimensional regression task (acne severity only) to a multi-dimensional task that includes severity prediction plus auxiliary classification dimensions (gender, ethnicity). This dimensional expansion allows the model to leverage correlations between demographic features and acne characteristics, improving grading accuracy by utilizing information that would otherwise be lost.
Data Source
AI summary
Systems and methods process images to determine a skin condition severity analysis and to visualize a skin analysis such as using a deep neural network (e.g. a convolutional neural network) where a problem was formulated as a regression task with integer-only labels. Auxiliary classification tasks (for example, comprising gender and ethnicity predictions) are introduced to improve performance. Scoring and other image processing techniques may be used (e.g. in assoc. with the model) to visualize results such as highlighting the analyzed image. It is demonstrated that the visualization of results, which highlight skin condition affected areas, can also provide perspicuous explanations for the model. A plurality (k) of data augmentations may be made to a source image to yield k augmented images for processing. Activation masks (e.g. heatmaps) produced from processing the k augmented images are used to define a final map to visualize the skin analysis.