Deep Learning Skin Diagnostics for Fast Image-Based Assessment
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Solution Overview
Problem
Existing skin analysis methods require human examination by dermatologists, which is time-consuming and costly, and there is a need for an automatic, accurate, and efficient system to analyze skin conditions from images using deep learning.
Innovation Solution
A deep learning-based system using a convolutional neural network (CNN) with a pre-trained encoder phase and decoder phase for skin sign diagnosis, incorporating a LeakyClamp activation function and ethnicity prediction, trained on diverse datasets to handle various lighting and pose conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human dermatologists examine skin in person, then diagnostic accuracy is maintained, but time consumption and cost increase
Solution Approach 1:
The patent creates a digital copy of the skin examination process through a convolutional neural network that analyzes images of skin conditions. The CNN model replicates the diagnostic functionality of human dermatologists by processing visual data and generating diagnoses, thereby eliminating the need for physical presence while maintaining diagnostic capabilities
Solution Approach 2:
The patent replaces the mechanical system of human visual inspection and physical examination with an automated computational system. The CNN-based diagnostic tool substitutes human dermatologists' manual analysis with algorithmic image processing, achieving both time efficiency and diagnostic accuracy through automated computation
2Measurement precision
If human dermatologists examine skin in person, then diagnostic accuracy is maintained, but cost increases
Solution Approach 1:
The patent creates a digital copy of the skin examination process through a convolutional neural network that analyzes images of skin conditions. The CNN model replicates the diagnostic functionality of human dermatologists by processing visual data and generating diagnoses, thereby eliminating the need for physical presence while maintaining diagnostic capabilities
Solution Approach 2:
The patent replaces the mechanical system of human visual inspection and physical examination with an automated computational system. The CNN-based diagnostic tool substitutes human dermatologists' manual analysis with algorithmic image processing, achieving both time efficiency and diagnostic accuracy through automated computation
3Measurement precision
If deep learning system uses pre-trained network and parallel branches, then diagnostic accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the skin diagnosis task into multiple parallel branches, each dedicated to detecting specific skin signs. This segmentation allows the system to specialize different parts of the network for different diagnostic functions, improving overall accuracy while organizing complexity into manageable, modular components
Solution Approach 2:
The patent applies preliminary action by using a pre-trained network for image classification as the foundation for the skin diagnosis system. This pre-training provides the model with general image understanding capabilities before fine-tuning for specific skin conditions, reducing the complexity of training from scratch while maintaining high diagnostic performance
Data Source
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AI summary
There is shown and described a deep learning based system and method for skin diagnostics as well as testing metrics that show that such a deep learning based system outperforms human experts on the task of apparent skin diagnostics. Also shown and described is a system and method of monitoring a skin treatment regime using a deep learning based system and method for skin diagnostics.