Intelligent framework for skin cancer detection and classification using deep learning

Al-Yaqoubi, Moza Atiyaq Obaid and Al-Hussaini, Mohammed Abdulla Salim and Alkishri, Wasin and Al Husaini, Yousuf Nasser and Habaebi, Mohamed Hadi and Al Masqari, Yasir Abdallah Khalifa (2026) Intelligent framework for skin cancer detection and classification using deep learning. In: 2026 IEEE 5th International Multidisciplinary Conference on Engineering Technology (IMCET), 15-17 April 2026, Beirut - Lebanon.

PDF - Published Version Restricted to Repository staff only Download (1MB) | Request a copy

Abstract

Skin cancer is one of the most common types of cancer worldwide, and catching it early can make a big difference in treatment success. In this paper, This study proposes a deep learning-based framework for skin cancer classification. Our approach involves carefully preprocessing images, augmenting data to improve the model, and then training CNN models through transfer learning. We tested our method on a dataset of 2,300 labeled skin lesion images, categorized into cancer and non-cancer groups. To see how well it works, we looked at various metrics like accuracy, sensitivity, specificity, precision, F1-score, and the area under the ROC curve (AUC). The results are promising: ResNet 18 achieved an impressive average accuracy of 96.8%, with sensitivity at 96.4%, specificity at 96.5%, precision at 96.7%, and an F1-score of 96.3% across different validation tests. It also scored an average AUC of 0.99, showing its strong ability to tell these two classes apart. These encouraging findings suggest that deep learning image classification can be a helpful tool for automated skin cancer analysis and could support doctors in making more informed diagnoses.

Item Type:	Proceeding Paper (Plenary Papers)
Uncontrolled Keywords:	Skin Cancer Detection, Image Classification, Deep Learning, Convolutional Neural Networks, Transfer Learning.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	19 May 2026 14:35
Last Modified:	19 May 2026 14:35
Queue Number:	2026-05-Q3452
URI:	http://irep.iium.edu.my/id/eprint/129061

Actions (login required)

View Item