Automated Brain Tumor Detection and Classification using VGG16: A Deep Learning Approach

Detecting and classifying brain tumors early is essential for timely treatment and improving survival rates. In this study, we present a method using the VGG16 deep learning model to automatically detect and classify brain tumors from MRI images. VGG16, known for its high accuracy in image classification, proves effective in identifying brain tumors. We preprocess MRI images using the Python Imaging Library (PIL) to ensure they are in the proper format before inputting them into the VGG16 model. Our approach aims to enhance the accuracy of brain tumor detection while significantly reducing the manual efforts required by medical professionals. Although challenges such as limited data availability and computational costs remain, this method shows promise in delivering faster and more precise tumor diagnosis. The study also reviews other techniques for brain tumor detection, highlighting their limitations and how our approach could help improve diagnostic practices and patient outcomes. Additionally, the model’s ability to be fine-tuned for various types of brain tumors further underscores its practical use in medical imaging.