Automated Brain Tumor Detection using Image Processing Techniques

Using MRI to reliably diagnose brain tumors is important but it is often time-consuming. The study uses an automated method for brain tumor detection and classification using image processing techniques and conventional machine learning algorithms in order to considerably accelerate the diagnostic procedure and automate the diagnosis of brain tumors. Through precise identification and classification of brain cancers in MRI images, the system provides a more efficient method of diagnosing brain tumors. The methodology consists of four main stages: data pre-processing, where images are prepared for analysis; application of various image filters such as bilateral, median, and Gaussian filters to enhance image quality and highlight tumor features; training of machine learning models including Logistic Regression and Support Vector Machines on pre-processed image data; and real-time prediction facilitated by a user-friendly graphical interface.