What is it about?
Abstract: BACKGROUND: Melanoma is a malignant skin cancer that causes high mortality. Early detection of melanoma can save patients’ lives. The features of the skin lesion images can be extracted using computer techniques to differentiate early between melanoma and benign skin lesions. OBJECTIVE: A new model of empirical wavelet decomposition (EWD) based on tan hyperbolic modulated filter banks (THMFBs) (EWD-THMFBs) was used to obtain the features of skin lesion images by MATLAB software. METHODS: The EWD-THMFBs model was compared with the empirical short-time Fourier decomposition method based on THMFBs (ESTFD-THMFBs) and the empirical Fourier decomposition method based on THMFBs (EFD-THMFBs). RESULTS: The accuracy rates obtained for EWD-THMFBs, ESTFD-THMFBs, and EFD-THMFBs models were 100%, 98.89%, and 83.33%, respectively. The area under the curve (AUC) was 1, 0.97, and 0.91, respectively. CONCLUSION: The EWD-THMFBs model performed best in extracting features from skin lesion images. This model can be programmed on a mobile to detect skin lesions in rural areas by a nurse before consulting a dermatologist.
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Why is it important?
New method of feature extraction using modified EFD
Perspectives
It is about classification of skin cancer. New method of feature extraction is applied.
Mohamed Moustafa Azmy
Alexandria University
Read the Original
This page is a summary of: Deep learning approach for skin melanoma and benign classification using empirical wavelet decomposition, Technology and Health Care, September 2024, IOS Press,
DOI: 10.3233/thc-240020.
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