Adamu, Shamsuddeen and Alhussian, Hitham and Aziz, Norshakirah and Abdulkadir, Said Jadid and Alwadin, Ayed and Abdullahi, Mujaheed and Garba, Aliyu (2025) Unleashing the power of Manta Rays Foraging Optimizer: A novel approach for hyper-parameter optimization in skin cancer classification. Biomedical Signal Processing and Control, 99. ISSN 17468094
Full text not available from this repository.Abstract
Optimizing hyperparameters is crucial for improving the performance of deep learning (DL) models, especially in complex applications like skin cancer classification from dermoscopic images. This study introduces a novel hyperparameter optimization strategy using the Manta Rays Foraging Optimizer (MRFO). A model tailored for skin cancer classification is created by fine-tuning a Convolutional Neural Network (CNN) with MRFO, coupled with in-depth image preprocessing. Empirical evaluations on diverse datasets (ISIC, PH2, HAM10000) showcase the significant superiority of the MRFO-based model over conventional optimization algorithms. The model achieves impressive accuracy and loss metrics (ISIC: 99.43 , 0.0250; PH2: 99.96 , 0.0033; HAM10000: 97.70 , 0.0626), outperforming alternative optimization algorithms such as the Grey Wolf Optimizer (98.33 accuracy, 0.17 loss), Whale Optimization Algorithm (96 accuracy), Grasshopper Optimization Algorithm (97.2 accuracy), Densnet121-MRFO (99.26 accuracy), InceptionV3 with GA (99.9 accuracy), and African Vulture Optimization Algorithm (92.7 accuracy). The novel approach demonstrates superior accuracy and loss metrics, underscoring its potential for precise and efficient skin cancer detection. Additionally, narrow confidence intervals and balanced precision-recall confirm the model's generalizability and effectiveness, paving the way for early and accurate skin cancer detection and potentially improving patient outcomes. © 2024 The Author(s)
Item Type: | Article |
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Impact Factor: | Cited by: 2; All Open Access, Hybrid Gold Open Access |
Uncontrolled Keywords: | Deep learning; Diseases; Cancer classification; Cancer detection; Convolutional neural network; Hyper-parameter; Hyper-parameter optimizations; Metaheuristic; Optimisations; Optimization algorithms; Optimizers; Skin cancers; african vulture optimization algorithm; algorithm; Article; cancer classification; comparative study; confidence interval; convolutional neural network; feature extraction; foraging behavior; grasshopper optimization algorithm; grey wolf optimizer; human; image processing; information technology; manta rays foraging optimizer; mathematical model; metaheuristics; sensitivity analysis; skin cancer; statistical analysis; treatment outcome; whale optimization algorithm; Convolutional neural networks |
Depositing User: | Mr Ahmad Suhairi Mohamed Lazim |
Date Deposited: | 08 Jul 2025 16:28 |
Last Modified: | 08 Jul 2025 16:28 |
URI: | http://scholars.utp.edu.my/id/eprint/38942 |