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Almaghthawi, Ahmed
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Improved deep learning architecture for skin cancer classification Owida, Hamza Abu; Alshdaifat, Nawaf; Almaghthawi, Ahmed; Abuowaida, Suhaila; Aburomman, Ahmad; Al-Momani, Adai; Arabiat, Mohammad; Chan, Huah Yong
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp501-508

Abstract

A leading cause of mortality globally, skin cancer is deadly. Early skin cancer diagnosis reduces mortality. Visual inspection is the main skin cancer diagnosis tool; however, it is imprecise. Researchers propose deep-learning techniques to assist physicians identify skin tumors fast and correctly. Deep convolutional neural networks (CNNs) can identify distinct objects in complex tasks. We train a CNN on photos with merely pixels and illness labels to classify skin lesions. We train on HAM-10000 using a CNN. On the HAM10000 dataset, the suggested model scored 95.23% efficiency, 95.30% sensitivity, and 95.91% specificity.
Co-Authors Abuowaida, Suhaila Aburomman, Ahmad Al-Momani, Adai Alshdaifat, Nawaf Arabiat, Mohammad Chan, Huah Yong Owida, Hamza Abu