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Dehkordi, AA, Neshat, M, Khosravian, A, Thilakaratne, M, Sadiq, AS 
ORCID: https://orcid.org/0000-0002-5746-0257 and Mirjalili, S,
2025.
Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning.
Scientific Reports, 15: 22586.
ISSN 2045-2322
Abstract
Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. However, Deep CNNs require substantial computational power and memory, particularly for large datasets and complex architectures. Additionally, optimising the hyperparameters of deep CNNs, although critical for enhancing model performance, is challenging due to the high computational costs involved, making it difficult without access to high-performance computing resources. To address these limitations, this study presents a fast and efficient model that aims to achieve superior classification performance compared to popular Deep CNNs by developing lightweight CNNs combined with the Nonlinear Lévy chaotic moth flame optimiser (NLCMFO) for automatic hyperparameter optimisation. NLCMFO integrates the Lévy flight, chaotic parameters, and nonlinear control mechanisms to enhance the exploration capabilities of the Moth Flame Optimiser during the search phase while also leveraging the Lévy flight theorem to improve the exploitation phase. To assess the efficiency of the proposed model, empirical analyses were performed using a dataset of 2314 brain tumour detection images (1245 images of brain tumours and 1069 normal brain images). The evaluation results indicate that the CNN_NLCMFO outperformed a non-optimised CNN by 5% (92.40% accuracy) and surpassed established models such as DarkNet19 (96.41%), EfficientNetB0 (96.32%), Xception (96.41%), ResNet101 (92.15%), and InceptionResNetV2 (95.63%) by margins ranging from 1 to 5.25%. The findings demonstrate that the lightweight CNN combined with NLCMFO provides a computationally efficient yet highly accurate solution for medical image classification, addressing the challenges associated with traditional deep CNNs.
| Item Type: | Journal article |
|---|---|
| Publication Title: | Scientific Reports |
| Creators: | Dehkordi, A.A., Neshat, M., Khosravian, A., Thilakaratne, M., Sadiq, A.S. and Mirjalili, S. |
| Publisher: | Springer Science and Business Media LLC |
| Date: | 2 July 2025 |
| Volume: | 15 |
| ISSN: | 2045-2322 |
| Identifiers: | Number Type 10.1038/s41598-025-02890-3 DOI 2474459 Other |
| Rights: | This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| Divisions: | Schools > School of Science and Technology |
| Record created by: | Jonathan Gallacher |
| Date Added: | 23 Jul 2025 14:47 |
| Last Modified: | 23 Jul 2025 14:47 |
| URI: | https://irep.ntu.ac.uk/id/eprint/54007 |
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