An optimized approach for brain tumor classification using MobileNet and enhanced Newton-Raphson optimizer

Abstract

Abstract This article introduces an enhanced version of the Newton-Raphson-based optimizer (I-NRBO) with a complex optimization technique termed the Local Escaping Operator (LEO). This method effectively balances solution variety with convergence speed, enhancing optimization effectiveness and reducing the likelihood of local optima. The research presents an advanced and efficient model that utilizes a pre-trained deep learning architecture, MobileNet, with the I-NRBO algorithm to enhance the classification accuracy of brain tumors. The I-NRBO method is evaluated over CEC-2022, the results showed that I-NRBO outperformed the traditional NRBO and other leading algorithms in terms of statistical convergence and various criteria. In the subsequent study, the proposed I-NRBO is applied to optimize hyperparameters in the MobileNet model, specifically for brain tumor classification. The I-NRBO-MobileNet model is assessed against many previous research and alternative deep learning architectures with comparable parameters, including VGG16, ResNet50, InceptionV3, and MobileNet as baseline models. The experimental findings reveal that the I-NRBO-MobileNet model outstanding performance, with an accuracy of 99.24%, a precision of 99.25%, a recall of 99.24%, an AUC of 99.95%, and a specificity of 99.70%. Results demonstrate that the suggested I-NRBO-MobileNet model can accurately classify brain cancers.