Comparative Analysis of Convolutional Neural Networks for Brain Tumor Detection: A Study of VGG16, ResNet, Inception, and DenseNet Models

This research work evaluates VGG16 against ResNet, Inception, and DenseNet models in a comparative analysis of convolutional neural network (CNN) architectures for brain tumour identification. The research study explores the effectiveness of these cutting-edge CNN architectures in precisely diagnosing brain tumours by utilising a collection of medical photographs. It is shown by thorough testing and analysis that VGG16 performs better in terms of classification accuracy than ResNet, Inception, and DenseNet models. The evaluation metrics encompassing accuracy, precision, recall, and F1-score highlight the superior performance of VGG16, showcasing its robustness and effectiveness in detecting brain tumors from medical images. Moreover, a comprehensive analysis of the computational complexity and training time associated with each model is conducted, providing insights into their practical viability in real-world applications. These findings contribute to the advancement of medical image analysis and provide valuable guidance for researchers and practitioners in selecting suitable CNN architectures for brain tumor detection tasks.