eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-06-30 18 2 1257 1271 10.13005/bpj/3167 65934 article Kamal Upreti 1 Jossy George 1 Khushboo Malik 2 Department of Computer Science and Engineering, Christ University, Delhi NCR, Ghaziabad, Uttar Pradesh, India Department of Law, Christ University, Delhi NCR, Ghaziabad, Uttar Pradesh, India Early and accurate classification of brain tumors plays a pivotal role in clinical decision-making and treatment planning. Manual methods are time-intensive and prone to variability, creating a need for robust automated solutions. This study aims to classify brain tumors from MRI scans using artificial intelligence techniques, specifically Logistic Regression (LR) and Support Vector Machines (SVM) with Radial Basis Function (RBF) kernels. The dataset, sourced from The Cancer Imaging Archive (TCIA), includes four classes: Meningioma, Glioma, Hypothalamic tumor, and No tumor. Preprocessing involved dimensionality reduction using Principal Component Analysis (PCA) to retain dominant features. Models were trained on an 80:20 train-test split, with LR achieving 99.83% training and 78.91% testing accuracy, while SVM performed better with 93.85% training and 81.88% testing accuracy. Error analysis revealed 104 misclassified samples, primarily due to structural similarity among tumor types. The findings suggest that SVM offers superior classification performance, and the study recommends further enhancement through deep learning models like Convolutional Neural Networks (CNNs) for improved diagnostic accuracy. https://biomedpharmajournal.org/vol18no2/automated-brain-tumor-segmentation-in-mri-using-ai-for-improved-neurodiagnostics/ Classification Diagnosis Early Detection Machine Learning MRI Neuro-images