Brain Tumor Detection Using Image Processing and Machine Learning

Abstract

Brain tumors represent one of the most critical neurological disorders affecting the central nervous system. Early and accurate detection of brain tumors is essential for effective treatment planning and improved patient survival rates. Medical imaging technologies such as Magnetic Resonance Imaging (MRI) provide detailed visualization of brain tissues; however, manual interpretation of MRI scans by radiologists is time-consuming and subject to human error. In recent years, image processing techniques combined with machine learning algorithms have emerged as efficient tools for automated tumor detection and classification. This research paper presents a systematic framework for detecting brain tumors using image processing and machine learning techniques. The proposed approach includes image preprocessing, segmentation, feature extraction, and classification stages. Techniques such as noise filtering, threshold-based segmentation, and machine learning classifiers are utilized to improve the detection accuracy. Experimental results demonstrate that machine learning models can significantly enhance the accuracy and reliability of brain tumor detection compared with traditional diagnostic approaches. The results highlight the effectiveness of the proposed methodology in assisting medical professionals in clinical decision-making.