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

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.