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Encoding Deep Residual Features into Fisher Vector for Skin Lesion Classification.

Hangyu Hu, Ziyang Chen, Yong Xia

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary

    This study introduces a new ResNet-FV algorithm for classifying skin lesions from dermoscopy images. This computer-aided diagnosis tool shows improved accuracy, aiding in early melanoma detection and potential large-scale skin cancer screening.

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    Area of Science:

    • Dermatology
    • Computer Science
    • Medical Imaging

    Background:

    • Early melanoma detection via dermoscopy is crucial for reducing mortality.
    • Computer-aided skin lesion classification faces challenges like small sample sizes and class imbalance.

    Purpose of the Study:

    • To develop a hybrid deep residual network and Fisher vector (ResNet-FV) algorithm for enhanced skin lesion classification.
    • To improve the performance of deep learning models in melanoma detection.

    Main Methods:

    • A hybrid deep residual network (ResNet) combined with Fisher vector (FV) encoding was proposed.
    • The ResNet-FV algorithm was evaluated on the ISIC-skin 2018 dataset.

    Main Results:

    • The ResNet-FV algorithm achieved a balanced multi-class accuracy of 0.798.
    • The proposed method outperformed several existing skin lesion classification solutions.

    Conclusions:

    • The ResNet-FV algorithm demonstrates superior performance for skin lesion classification.
    • This computer-aided diagnosis tool has potential for large-scale skin cancer screening.