Melanoma Detection From Lesion Images Using Optimized Features Selected by Metaheuristic Algorithms

Mukherjee, Soumen; Adhikari, Arunabha; Roy, Madhusudan

doi:10.4018/ijhisi.288542

Cited by 5 publications

(2 citation statements)

References 47 publications

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“…Moreover, the ACO approach can prove significant potential for continuous operation and adaptation to various changes associated with real-time data analysis. These advantages have captured the interest of scholars and researchers engaged in active classification activities, graphical data investigation, and visual image detection in medical, engineering, and mathematical domains [20,21].…”

Section: Crucial Features and Practical Merits Of Aco For The Medical...mentioning

confidence: 99%

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

Mukhlif,

Ramaha,

Hameed

et al. 2024

Mathematics

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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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Section: Crucial Features and Practical Merits Of Aco For The Medical...mentioning

confidence: 99%

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

Mukhlif,

Ramaha,

Hameed

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…To determine the shortest path from their location to the food supply and the vice versa, ants search using a metaheuristic algorithm called ACO [55] [56]. An ant will leave a trail of a specific chemical pheromone on its way of travel when it leaves its nest in search of food.…”

Section: ) Ant Colony Optimization (Aco)mentioning

confidence: 99%

An Approach for Optimization of Features using Gorilla Troop Optimizer for Classification of Melanoma

Damarla¹,

Sumathi²

2022

IJACSA

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The diagnosis and categorization of skin cancer, as well as the difference in skin textures and injuries, is a tough undertaking. Manually detecting skin lesions from dermoscopy images seems to be a difficult and cumbersome challenge. Recent advancements in the internet of things (IoT) and artificial intelligence for clinical applications have shown significant increase in precision and processing time. A lot of attention is given to deep learning models because they are effective at identifying cancer cells. The diagnosis and accuracy levels can be greatly increased by categorizing benign and malignant dermoscopy images. This work suggests an automated classification system based on a deep convolutional neural network (DCNN) in order to precisely perform multiclassification. The DCNN's structure was thoughtfully created by arranging a number of layers that are in charge of uniquely extracting different features from skin lesions. In this paper, we proposed a deep learning approach to tackle the three main tasks-deep extraction of features (task1) using transfer learning, selection of features (task2)-using metaheuristic algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Gorilla Troop Optimization (GTO) as a feature selector, the extensive feature set is optimized, and the amount of features is reduced to within the range, and a two-level classification (task3) was proposed that are emerging in the field of skin lesion image processing. On the HAM10000 dataset, the proposed deep learning frameworks were assessed. The accuracy achieved on the dataset is 93.58 percent. The proposed method outperforms state-of-the-art (SOTA) techniques in terms of accuracy. The suggested technique is however highly scalable.

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A hybrid deep learning skin cancer prediction framework

Farea,

Saleh,

AbuAlkebash

et al. 2024

Engineering Science and Technology, an International Journal

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Melanoma Detection From Lesion Images Using Optimized Features Selected by Metaheuristic Algorithms

Cited by 5 publications

References 47 publications

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

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

An Approach for Optimization of Features using Gorilla Troop Optimizer for Classification of Melanoma

A hybrid deep learning skin cancer prediction framework

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