A Machine Learning-Based Framework for Brain Tumor Classification from MRI Scans

Brain tumors are amongst the neurological disordersthatareofhighconcern.Earlyandaccuratediagnosis is a high priority for improvement in patient survival rates. Manual MRI image analysis is time-consuming and highly dependent on expert radiologists. In the present work, the authorsproposeNeuroScan-afullyautomatedsystemforbrain tumordetectionandclassification basedonamachine-learning framework. MRI image quality is enhanced by resizing, grayscale conversion, noise reduction, and Min-Max normalization. Next, the features were extracted and classification was performed using SVM and Logistic Regression. The dataset consisted of approximately 2500 brain MRI images, which are collected from publicly available repositories. The experimental results indicated the better performanceoftheSVMclassifierwithaccuracy upto93-95% outperforming Logistic Regression. Therefore, this system greatly assists clinicians by offering more efficient, consistent, and high-speed tumor detection, aiding in early diagnosis and treatment planning. The results have demonstrated the efficiency and feasibility of such machine learning-based systemsfor medical image analysis and clinical decision support.