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

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 optimizati...

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Vydáno v:Mathematics (Basel) Ročník 12; číslo 7; s. 1049
Hlavní autoři: Mukhlif, Yasir Adil, Ramaha, Nehad T. A., Hameed, Alaa Ali, Salman, Mohammad, Yon, Dong Keon, Fitriyani, Norma Latif, Syafrudin, Muhammad, Lee, Seung Won
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.04.2024
Témata:
Accuracy
Algorithms
Ant colony optimization
ant colony optimization (ACO)
Artificial intelligence
Cetacea
Classification
Clinical outcomes
Comparative analysis
Decision making
Deep learning
Dermatology
Efficiency
feature selection
Forecasts and trends
Lesions
Machine learning
Mathematical optimization
Medical imaging equipment
Neural networks
neural networks (NNs)
Optimization algorithms
Peer review
Skin
skin lesion disorder (SLD)
whale optimization algorithm (WOA)
ISSN:2227-7390, 2227-7390
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Shrnutí: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12071049