Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

Yasir Adil Mukhlif, Nehad T. A. Ramaha, Alaa Ali Hameed, Mohammad Salman, Dong Keon Yon, Norma Latif Fitriyani (), Muhammad Syafrudin () and Seung Won Lee ()
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Yasir Adil Mukhlif: Department of Computer Engineering, Karabuk University, Demir Celik Campus, 78050 Karabuk, Turkey
Nehad T. A. Ramaha: Department of Computer Engineering, Karabuk University, Demir Celik Campus, 78050 Karabuk, Turkey
Alaa Ali Hameed: Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, 34396 Istanbul, Turkey
Mohammad Salman: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Dong Keon Yon: Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University, Seoul 02453, Republic of Korea
Norma Latif Fitriyani: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
Muhammad Syafrudin: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
Seung Won Lee: Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea

Mathematics, 2024, vol. 12, issue 7, 1-29

Abstract: The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients.

Keywords: skin lesion disorder (SLD); ant colony optimization (ACO); whale optimization algorithm (WOA); neural networks (NNs); feature selection; classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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