eprintid: 19081
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/90/81
datestamp: 2024-06-04 14:11:31
lastmod: 2024-06-04 14:11:31
status_changed: 2024-06-04 14:04:48
type: article
metadata_visibility: show
creators_name: Adamu, S.
creators_name: Alhussian, H.
creators_name: Aziz, N.
creators_name: Abdulkadir, S.J.
creators_name: Alwadin, A.
creators_name: Imam, A.A.
creators_name: Garba, A.
creators_name: Saidu, Y.
title: Optimizing Hyperparameters for Improved Melanoma Classification using Metaheuristic Algorithm
ispublished: pub
keywords: Convolutional neural networks; Cost effectiveness; Dermatology; Learning algorithms; Oncology; Optimization, Convolutional neural network; Deep learning; Healthcare systems; Hyper-parameter; Machine-learning; Meta-heuristics algorithms; Optimisations; Optimizers; Skin cancers; Survival rate, Deep neural networks
note: cited By 0
abstract: Melanoma, a prevalent and formidable skin cancer, necessitates early detection for improved survival rates. The rising incidence of melanoma poses significant challenges to healthcare systems worldwide. While deep neural networks offer the potential for precise melanoma classification, the optimization of hyperparameters remains a major obstacle. This paper introduces a groundbreaking approach that harnesses the Manta Rays Foraging Optimizer (MRFO) to empower melanoma classification. MRFO efficiently fine-tunes hyperparameters for a Convolutional Neural Network (CNN) using the ISIC 2019 dataset, which comprises 776 images (438 melanoma, 338 non-melanoma). The proposed cost-effective DenseNet121 model surpasses other optimization methods in various metrics during training, testing, and validation. It achieves an impressive accuracy of 99.26, an AUC of 99.56, an F1 score of 0.9091, a precision of 94.06, and a recall of 87.96. Comparative analysis with EfficientB1, EfficientB7, EfficientNetV2B0, NesNetLarge, ResNet50, VGG16, and VGG19 models demonstrates its superiority. These findings underscore the potential of the novel MRFO-based approach in achieving superior accuracy for melanoma classification. The proposed method has the potential to be a valuable tool for early detection and improved patient outcomes. © (2023) All Rights Reserved.
date: 2023
publisher: Science and Information Organization
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175302308&doi=10.14569%2fIJACSA.2023.0141057&partnerID=40&md5=d2ffdb41787f04daf0b44b7e882c780b
id_number: 10.14569/IJACSA.2023.0141057
full_text_status: none
publication: International Journal of Advanced Computer Science and Applications
volume: 14
number: 10
pagerange: 531-540
refereed: TRUE
issn: 2158107X
citation:   Adamu, S. and Alhussian, H. and Aziz, N. and Abdulkadir, S.J. and Alwadin, A. and Imam, A.A. and Garba, A. and Saidu, Y.  (2023) Optimizing Hyperparameters for Improved Melanoma Classification using Metaheuristic Algorithm.  International Journal of Advanced Computer Science and Applications, 14 (10).  pp. 531-540.  ISSN 2158107X