Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning.
Authors
Affiliations (7)
Affiliations (7)
- Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. [email protected].
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD, 4006, Australia.
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Sydney, NSW, 2007, Australia.
- School of Computer Science, The University of Adelaide, Adelaide, SA, 5005, Australia.
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
- Yonsei Frontier Lab, Yonsei University, Seoul, South Korea.
- University Research and Innovation Center, Obuda University, Budapest, 1034, Hungary.
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.