Brain Tumor Detection Approach Based on VGG16 and Transfer Learning

Artificial intelligence has become an invaluable tool in various fields, especially in medicine, where it significantly aids in disease detection. Early detection of brain tumors is critical for improving patient survival rates and optimizing therapeutic strategies. In this study, we propose an approach based on the pre-trained VGG16 model, leveraging transfer learning to identify the presence of tumors in brain Magnetic Resonance Imaging (MRI) scans. The dataset used, Brain MRI Images for Brain Tumor Detection, provides a diverse range of images that support robust and relevant model training. A fine-tuning phase was incorporated to tailor the model to the specificities of medical data. Experimental results demonstrate an accuracy exceeding 86%, highlighting the effectiveness of this proposed approach for automated brain tumor detection.This study paves the way for promising prospects, particularly in terms of methodological improvements and deployment in real clinical settings.