Brain Tumor Detection Using Deep Learning (CNNs)

This research explores the potential of Convolutional Neural Networks (CNNs) for brain tumor detection. It evaluates various models like CNN simple and VGG 16. The proposed system follows a three-step process: image preprocessing, feature extraction through the CNN, and final classification using fully connected layers to categorize MRI images as normal or tumor-containing. Deep Learning (DL), a powerful subfield of Artificial Intelligence, utilizes various techniques for efficient automated learning from any data type. This project investigates the potential of DL in predicting cancer onset, specifically focusing on brain tumors. While medical imaging remains a cornerstone of brain tumor detection, interpreting these images and segmenting tumors can be challenging and subjective. Automatic detection methods powered by Deep Learning have shown promise, but there's room for improvement in accuracy and reliability. Therefore, this work aims to address the critical question: how can Deep Learning techniques be employed to enhance brain tumor detection in medical imaging?