Open Access

ARTICLE

A Multi-Layers Information Fused Deep Architecture for Skin Cancer Classification in Smart Healthcare

Veena Dillshad¹, Muhammad Attique Khan^2,*, Muhammad Nazir¹, Jawad Ahmad², Dina Abdulaziz AlHammadi³, Taha Houda², Hee-Chan Cho⁴, Byoungchol Chang^5,*

1 Department of Computer Science, HITEC University, Taxila, 47080, Pakistan
2 Department of Artificial Intelligence, College of Computer Engineering and Science, Prince Mohammad Bin Fahd University, Al-Khobar, 31952, Saudi Arabia
3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 Center for Computational Social Science, Hanyang University, Seoul, 01000, Republic of Korea
5 Department of Computer Science, Hanynag University, Seoul, 01000, Republic of Korea

* Corresponding Authors: Muhammad Attique Khan. Email: ; Byoungchol Chang. Email:

(This article belongs to the Special Issue: Deep Learning and IoT for Smart Healthcare)

Computers, Materials & Continua 2025, 83(3), 5299-5321. https://doi.org/10.32604/cmc.2025.063851

Received 25 January 2025; Accepted 28 February 2025; Issue published 19 May 2025

Abstract

Globally, skin cancer is a prevalent form of malignancy, and its early and accurate diagnosis is critical for patient survival. Clinical evaluation of skin lesions is essential, but several challenges, such as long waiting times and subjective interpretations, make this task difficult. The recent advancement of deep learning in healthcare has shown much success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics. Deep learning improves the speed and precision of skin cancer diagnosis, leading to earlier prediction and treatment. In this work, we proposed a novel deep architecture for skin cancer classification in innovative healthcare. The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset. The proposed architecture is based on two customized, innovative Convolutional neural network (CNN) models based on small depth and filter sizes. In the first model, four residual blocks are added in a squeezed fashion with a small filter size. In the second model, five residual blocks are added with smaller depth and more useful weight information of the lesion region. To make models more useful, we selected the hyperparameters through Bayesian Optimization, in which the learning rate is selected. After training the proposed models, deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique. The final features are passed on to the classifiers, and classification results are obtained. Also, the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset. The experimental process of the proposed architecture is performed on two dermoscopic datasets, HAM10000 and ISIC2019. We obtained an improved accuracy of 90.8% and 99.3% on these datasets, respectively. Also, the proposed architecture returned 91.6% for the cancer localization. In conclusion, the proposed architecture accuracy is compared with several pre-trained and state-of-the-art (SOTA) techniques and shows improved performance.

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Keywords

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