Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning

Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning

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 computati...

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Bibliographic Details
Main Authors: Amin Abdollahi Dehkordi, Mehdi Neshat, Alireza Khosravian, Menasha Thilakaratne, Ali Safaa Sadiq, Seyedali Mirjalili
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Convolutional neural networks (CNN)
Nonlinear Lévy chaotic moth flame optimiser (NLCMFO)
Image classification
Optimization
Online Access:https://doi.org/10.1038/s41598-025-02890-3
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