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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Multi-Class Skin Lesion Detection and Classification via Teledermatology.

Muhammad Attique Khan, Khan Muhammad, Muhammad Sharif

    IEEE Journal of Biomedical and Health Informatics
    |March 22, 2021
    PubMed
    Summary

    This study introduces a novel framework for classifying skin lesions using teledermatology. The system effectively segments and classifies skin lesions from images, improving diagnostic accuracy for remote healthcare applications.

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    Area of Science:

    • Dermatology
    • Medical Imaging
    • Computer Science

    Background:

    • Teledermatology leverages telecommunication for remote medical information transfer, proving effective for skin lesion diagnosis, especially in rural areas.
    • It aids in reducing unnecessary clinical referrals and triaging dermatology cases.
    • Accurate classification of skin lesions is crucial for timely and effective treatment.

    Purpose of the Study:

    • To develop and evaluate a robust framework for classifying skin lesion images obtained via teledermatology.
    • To enhance the accuracy of skin lesion diagnosis through automated segmentation and classification techniques.

    Main Methods:

    • A hybrid approach combining a 16-layered convolutional neural network and high-dimension contrast transform (HDCT) for skin lesion localization and segmentation.
    • Utilizing a maximal mutual information method for optimal lesion image extraction.
    • Employing transfer learning with a pre-trained DenseNet201 model for classification, followed by t-distribution stochastic neighbor embedding (t-SNE) for feature reduction and multi-canonical correlation analysis (MCCA) for feature fusion with an extreme learning machine (ELM) classifier.

    Main Results:

    • The proposed framework demonstrated strong performance in the segmentation of skin lesions across multiple datasets (ISBI2016, ISIC2017, PH2, ISBI2018).
    • The classification module, evaluated on the HAM10000 dataset, achieved state-of-the-art results, affirming the framework's efficacy.
    • The hybrid segmentation and advanced classification approach proved effective in handling complex dermatological image data.

    Conclusions:

    • The developed teledermatology framework offers a significant advancement in automated skin lesion analysis.
    • The integrated approach of hybrid segmentation and deep learning-based classification provides a powerful tool for dermatological diagnostics.
    • This research supports the expanded use of teledermatology for improved skin health management, particularly in underserved regions.