Real-Time Medical Image Classification Using Deep Learning
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Solution Overview
Problem
Conventional methods for object detection and classification in medical imaging are time-consuming, prone to human error, and lack adaptability and learning capability to handle diverse and challenging scenarios effectively, especially in real-time medical procedures where accurate and immediate identification of abnormalities is critical.
Innovation Solution
A method and system utilizing deep learning techniques, including an autoencoder-based model for correcting reflections, a Single Shot Detection model for region-of-interest determination, and a Convolution Neural Network for classification, to process real-time medical images and classify objects as cancerous, pre-cancerous, or non-cancerous types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection by radiologists is used for object detection and classification, then diagnostic accuracy can be maintained, but the process becomes time-consuming and is subject to human error
Solution Approach 1:
The patent replaces the mechanical system of manual visual inspection by radiologists with an automated electronic image processing system that uses deep learning models (autoencoder, SSD, and CNN) to detect and classify medical objects, thereby eliminating human error and significantly reducing time consumption while maintaining diagnostic accuracy
Solution Approach 2:
The system enables self-service through autonomous deep learning models that automatically perform detection and classification without human intervention, with the autoencoder correcting distortions, SSD identifying regions of interest, and CNN classifying objects independently and rapidly
2Adaptability or versatility
If conventional detection methods are used, then system complexity can be kept low, but adaptability and learning capability to handle diverse scenarios are insufficient
Solution Approach 1:
The patent employs multiple deep learning models with different architectures and functions (autoencoder for distortion correction, SSD for detection, CNN for classification) that can be selectively applied based on the specific medical imaging scenario, providing high adaptability through parameter and model selection while managing complexity through modular design
Solution Approach 2:
The system segments the complex detection and classification task into three distinct modules: autoencoder for pre-processing and distortion correction, SSD for region of interest detection, and CNN for classification, allowing each component to be optimized independently and improving overall adaptability to diverse medical imaging scenarios
3Speed
If real-time analysis is required during medical procedures, then immediate identification of critical objects is achieved, but conventional methods lack the processing speed and learning capability
Solution Approach 1:
The autoencoder performs preliminary action by pre-processing and correcting distortions in image frames before they are passed to the SSD and CNN models, enabling real-time processing with improved accuracy by preparing the data in advance for faster and more reliable detection and classification
Solution Approach 2:
The system maintains continuous real-time analysis during medical procedures by processing image frames sequentially as they are captured, with the deep learning models operating continuously to provide immediate identification of critical objects without interruption, ensuring both speed and reliability
Data Source
AI summary
A method for detecting and classifying an object is disclosed. The method includes receiving imaging data captured by imaging device. Further, the method includes generating a pre-processed image frame by correcting one or more pixels corresponding to reflections in corresponding image frame using autoencoder based DL model. Further the corrected image is split into R channel image, G channel image, and B channel image. Further, texture enhancement of G channel image and denoising the B channel image using wiener filter is performed to generate a color enhanced image frame. Further, regions of interest are determined corresponding to at least one object in the pre-processed image frame using SSD model. Further, the at least one object is classified as one of: cancerous type, pre-cancerous type or non-cancerous type using a CNN model.