Explainable Skin Lesion Classification Using ABCD Feature Analysis
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Solution Overview
Problem
Current systems lack an effective and interpretable method for early detection of skin cancer using artificial intelligence, as existing AI models lack explainability and struggle to accurately classify various skin lesions, including sub-types, leading to potential misdiagnosis and low trust among practitioners.
Innovation Solution
A compute system utilizing Explainable AI (XAI) for skin lesion analysis that segments lesions, applies the ABCD rule and 7-point checklist, and provides a normalized image with risk level assessment, enhancing interpretability and accuracy through hierarchical learning and dermoscopic structure analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI models are used for skin lesion classification, then detection accuracy is improved, but model interpretability deteriorates
Solution Approach 1:
The patent segments the skin lesion analysis into distinct components: lesion segmentation, ABCD rule analysis (Asymmetry, Border, Color, Diameter), and 7-point checklist evaluation. This segmentation allows each component to be independently analyzed and explained, providing interpretability while maintaining overall detection accuracy through the integrated AI model.
Solution Approach 2:
The patent introduces an intermediary explanation layer that bridges the AI model's black-box predictions and human understanding. This intermediary provides detailed reasoning based on dermatological principles (ABCD rule and 7-point checklist), translating model outputs into interpretable clinical assessments that maintain both accuracy and transparency.
2Measurement precision
If comprehensive skin lesion classification is performed, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the comprehensive classification task into 10 distinct skin cancer classes and 122 sub-classes, analyzing specific dermatological features (asymmetry, border, color, diameter, and 7-point checklist criteria) separately. This segmentation enables the system to handle complex classification through structured, manageable components rather than a monolithic complex system.
Solution Approach 2:
The patent adds multiple analysis dimensions to the classification system: traditional ABCD rule analysis plus the 7-point checklist evaluation, creating a multi-dimensional assessment framework. This dimensional expansion allows comprehensive classification of 10 classes and 122 sub-classes while organizing complexity through structured hierarchical categories.
3Reliability
If early detection capability is enhanced, then patient outcomes are improved, but false positive rate may increase
Solution Approach 1:
The patent incorporates feedback mechanisms through the 7-point checklist evaluation and ABCD rule analysis, where each dermatological feature is systematically assessed and fed back into the classification decision. This multi-stage feedback process allows the system to refine its predictions and reduce false positives while maintaining high early detection capability across 10 classes and 122 sub-classes.
Data Source
AI summary
A method of operation of a compute system includes: segmenting a skin lesion in a patient image; constructing a normalized image by cropping the patient image and adding padding to position the skin lesion at a center of the normalized image, identifying a skin lesion classification, a skin lesion sub-class, and a risk level assessment by analyzing a symmetry axis, a border, color variation, and dermoscopic structures, and generating a skin lesion display including the normalized image, the skin lesion classification, the skin lesion sub-class, and the risk level assessment for displaying on a device.