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    This study introduces a new Computer Aided-Diagnosis (CAD) system for melanoma detection that uses clinically inspired color descriptions. The system enhances dermatologist trust by providing understandable explanations for its diagnostic decisions.

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    Area of Science:

    • Dermatology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Computer Aided-Diagnosis (CAD) systems aid dermatologists in melanoma diagnosis.
    • Current CAD systems lack medical explanations, hindering user trust and adoption.
    • Dermatologists require understandable systems for effective clinical integration.

    Purpose of the Study:

    • To develop a novel CAD system for melanoma diagnosis.
    • To enhance system transparency by extracting clinically relevant color descriptions.
    • To improve dermatologist confidence through comprehensible diagnostic reasoning.

    Main Methods:

    • A CAD system was developed to extract clinically inspired color features from skin lesions.
    • An image annotation framework using Correspondence-LDA was employed to address data limitations.
    • The extracted color features were used to classify melanomas versus benign lesions.

    Main Results:

    • The Correspondence-LDA algorithm achieved 84.9% precision and 85.5% recall in identifying relevant colors.
    • The developed CAD system demonstrated a sensitivity of 78.9% and specificity of 76.7% in classifying skin lesions.
    • These performance metrics are comparable to existing state-of-the-art methods.

    Conclusions:

    • The proposed CAD system offers a more interpretable approach to melanoma diagnosis.
    • Clinically inspired color descriptions enhance the comprehensibility of AI-driven dermatological tools.
    • This work advances the development of trustworthy AI in clinical dermatology.