Artificial Intelligence with Deep Learning Driven Attention-Guided Temporal Convolutional Network for Skin Cancer Detection in Biomedical Images

Manar Ahmed Hamza; Ameerah Almutiry

doi:10.48084/etasr.18371

Authors

Manar Ahmed Hamza Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
Ameerah Almutiry Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

Volume: 16 | Issue: 3 | Pages: 35263-35269 | June 2026 | https://doi.org/10.48084/etasr.18371

Received: 25 February 2026 | Revised: 22 March 2026 | Accepted: 1 April 2026 | Online: 20 May 2026

Corresponding author: Manar Ahmed Hamza

Abstract

Skin diseases are a widespread global health concern, as pathogenic agents lead to both physical illness and psychological distress and, in severe cases, may result in skin cancer. Sustainable Development Goal (SDG) 3, which refers to Good Health and Well-Being, aims to ensure healthy lives and promote well-being for people of all ages, with a focus on reducing mortality from diseases. However, accurate identification of skin conditions from clinical imagery remains a significant challenge in medical image analysis, which has led to the development of various early detection technologies. Recently, image-based detection has witnessed considerable progress due to advances in Deep Learning (DL). DL-enabled approaches have shown outstanding performance in classifying and segmenting skin lesions due to their potential to capture intricate features from skin lesion imagery with high precision. This article presents a Feature Fusion and Attention-Guided Temporal Convolutional Neural Network for Skin Cancer Detection (FFATCN-SCD) framework, which aims to develop an efficient and intelligent system for accurate skin cancer identification and classification. The FFATCN-SCD technique initially performs image preprocessing using a Bilateral Filter (BF) to enhance image quality by reducing noise, followed by feature extraction using the fusion of EfficientNetV2 and InceptionV3 to capture detailed features that contribute to efficient detection. Subsequently, a Temporal Convolutional Network (TCN) with an Attention Mechanism (AM) is utilized to effectively classify skin cancer, and Antlion Optimization (ALO) is applied for optimal parameter tuning of the classification network to ensure improved model performance. To demonstrate the superior performance of the FFATCN-SCD approach, comprehensive simulations are conducted, and the results are evaluated using several metrics. Comparative analysis further demonstrates the superiority of the FFATCN-SCD model across various evaluation metrics.

Keywords:

skin cancer, EfficientNetV2, Antlion Optimization (ALO), Temporal Convolutional Network (TCN), Deep Learning (DL), InceptionV3

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