Modified Whale Optimization Algorithm for Multiclass Skin Cancer Classification

Abdul Majid (), Masad A. Alrasheedi, Abdulmajeed Atiah Alharbi, Jeza Allohibi and Seung-Won Lee ()
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Abdul Majid: Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan
Masad A. Alrasheedi: Department of Management Information Systems, College of Business Administration, Taibah University, Al-Madinah Al-Munawara 42353, Saudi Arabia
Abdulmajeed Atiah Alharbi: Department of Mathematics, Faculty of Science, Taibah University, Al-Madinah Al-Munawara 42353, Saudi Arabia
Jeza Allohibi: Department of Mathematics, Faculty of Science, Taibah University, Al-Madinah Al-Munawara 42353, Saudi Arabia
Seung-Won Lee: Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea

Mathematics, 2025, vol. 13, issue 6, 1-21

Abstract: Skin cancer is a major global health concern and one of the deadliest forms of cancer. Early and accurate detection significantly increases the chances of survival. However, traditional visual inspection methods are time-consuming and prone to errors due to artifacts and noise in dermoscopic images. To address these challenges, this paper proposes an innovative deep learning-based framework that integrates an ensemble of two pre-trained convolutional neural networks (CNNs), SqueezeNet and InceptionResNet-V2, combined with an improved Whale Optimization Algorithm (WOA) for feature selection. The deep features extracted from both models are fused to create a comprehensive feature set, which is then optimized using the proposed enhanced WOA that employs a quadratic decay function for dynamic parameter tuning and an advanced mutation mechanism to prevent premature convergence. The optimized features are fed into machine learning classifiers to achieve robust classification performance. The effectiveness of the framework is evaluated on two benchmark datasets, PH2 and Med-Node, achieving state-of-the-art classification accuracies of 95.48% and 98.59%, respectively. Comparative analysis with existing optimization algorithms and skin cancer classification approaches demonstrates the superiority of the proposed method in terms of accuracy, robustness, and computational efficiency. Our method outperforms the genetic algorithm (GA), Particle Swarm Optimization (PSO), and the slime mould algorithm (SMA), as well as deep learning-based skin cancer classification models, which have reported accuracies of 87% to 94% in previous studies. A more effective feature selection methodology improves accuracy and reduces computational overhead while maintaining robust performance. Our enhanced deep learning ensemble and feature selection technique can improve early-stage skin cancer diagnosis, as shown by these data.

Keywords: skin cancer; deep learning; feature extraction; optimization; classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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