Brain tumor diagnosis from MRI based on Mobilenetv2 optimized by contracted fox optimization algorithm

Lu Xu; Morteza Mohammadi

doi:10.1016/j.heliyon.2023.e23866

Brain tumor diagnosis from MRI based on Mobilenetv2 optimized by contracted fox optimization algorithm

Heliyon. 2023 Dec 19;10(1):e23866. doi: 10.1016/j.heliyon.2023.e23866. eCollection 2024 Jan 15.

Authors

Lu Xu¹, Morteza Mohammadi^{2

3}

Affiliations

¹ Department of Interventional Therapy, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230000, China.
² Department of Medical Sciences, Tehran Branch, Islamic Azad University, Tehran, Iran.
³ College of Technical Engineering, The Islamic University, Najaf, Iraq.

Abstract

This research paper presents an innovative approach to brain tumor diagnosis using MRI scans, using the power of deep learning and metaheuristic algorithm. The study employs Mobilenetv2, a deep learning model, optimized by a novel metaheuristic known as the Contracted Fox Optimization Algorithm (MN-V2/CFO). This methodology allows for the optimal selection of Mobilenetv2 hyperparameters, enhancing the accuracy of tumor detection. The model is implemented on the Figshare dataset, a comprehensive collection of MRI scans, and its performance is validated against other processes the results are compared with some published works including Network (RN), wavelet transform, and deep learning (WT/DL), customized VGG19, and Convolutional neural network (CNN). The results of the study, highlight the superior performance of the proposed MN-V2/CFO model compared to other tactics. The recommended strategy achieves a precision of 97.68 %, an F1-score of 86.22 %, a sensitivity of 80.12 %, and an accuracy of 97.32 %. The findings validate the potential of the proposed model in revolutionizing brain tumor diagnosis, contributing to better treatment strategies, and improving patient outcomes.

Keywords: Brain tumor; Contracted fox optimization algorithm; Deep learning; Diagnois; Figshare dataset; MRI; Medical imaging; Mobilenetv2.