Enhancing Brain Tumor Detection in MRI Images Through Fine-Tuning of VGG 16 Networks: A Precision Diagnostic Approach

This paper explores the impact of fine-tuning on brain tumour diagnosis using MRI data and a pre-trained VGG 16 model on ImageNet. Our objectives in fine-tuning the model were to improve its generalisation ability and diagnostic accuracy. Our results demonstrate a considerable improvement in the model's performance, with decreased validation loss and increased validation accuracy. The findings show that, despite the possibility of overfitting, fine-tuning is a helpful technique for deep learning model optimisation in medical image analysis. By enhancing diagnostic skills in healthcare, particularly in the early detection of brain tumours, this tactic may enhance patient outcomes.