Leveraging Lightweight Pretrained Model for Brain Tumour Detection

Abstract

This study presents an analysis of two deep learning models deployed for brain tumour detection: the lightweight pretrained MobileNetV2 and a novel hybrid model by combining light-weight MobileNetV2 with VGG16. The aim is to investigate the performance and efficiency of these models in terms of accuracy and training time. The new hybrid model integrates the strengths of both architectures, leveraging the depth-wise separable convolutions of MobileNetV2 and the deeper feature extraction capabilities of VGG16. Through experimentation and evaluation using a publicly available benchmark brain tumour dataset, the results demonstrate that the hybrid model achieves superior accuracy of training and testing accuracy of 99% and 98%, respectively compared to the standalone MobileNetV2 model, even at lower epochs. This novel fusion model presents a promising approach for enhancing brain tumour detection systems, offering improved accuracy with reduced training time and computational resources.