An AI-Powered Web Application for Automating Brain Tumor Identification

Brain tumors are life-threatening conditions caused by the unregulated expansion of abnormal brain cells, classified as either benign or malignant. Early detection is vital for successful treatment, but accurate diagnosis is often difficult because they differ in cell types, shape, and texture. Traditionally, brain tumors are detected manually by analyzing MRI images, a method that is time-consuming and subject to human error. With advancements in artificial intelligence, deep learning has emerged as a powerful tool to automate and improve the accuracy of brain tumor diagnosis. This study introduces a web-based application designed to detect and classify brain tumors from MRI images using deep learning methods. Initially, image preprocessing is performed to optimize the MRI dataset, followed by the use of a Convolutional Neural Network (CNN) to accurately classify the images as either tumor or non-tumor. The proposed model aims to improve diagnostic accuracy and provide visual interpretability, facilitating early detection and aiding medical professionals in treatment planning. The results demonstrate the model’s potential to support efficient and reliable brain tumor diagnosis.