Brain Tumor Detection–Using Medical Imaging and Machine Learning Techniques: A Python-Based Approach

The diagnosis of brain tumors using magnetic resonance imaging (MRI) remains a critical yet challenging task in the medical field. Traditional diagnostic procedures depend heavily on the expertise of radiologists, often resulting in delays and subjectivity. This research presents a Python-based automated framework that utilizes artificial intelligence and machine learning for accurate tumor detection. Key image features are extracted using Gray-Level Co-occurrence Matrix (GLCM) and Histogram of Oriented Gradients (HOG), which are then classified using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The developed CNN model demonstrated high performance with an accuracy of 97.5%, indicating its viability for supporting clinical diagnostics through efficient and consistent tumor identification.