CNN, SVM, LR, Brain Tumor Classification, MRI, Machine Learning


In the realm of medical diagnostics, precise classification of brain tumors is pivotal. This study conducts a comprehensive comparative analysis of a Convolutional Neural Network (CNN) against traditional machine learning models, Logistic Regression (LR) and Support Vector Machines (SVM) on a dataset of MRI scans for multi-class brain tumor classification. The CNN, tailored for image recognition, is evaluated alongside LR and SVM, which have established benchmarks in classification tasks. The investigation reveals that the traditional models hold their ground in terms of precision and interpretability, with the SVM, in particular, achieving remarkable accuracy. However, the CNN distinguishes itself by demonstrating superior performance and high confidence in its predictions, highlighting the advantages of deep learning for complex pattern recognition in neuroimaging. These insights signify a substantial stride towards integrating advanced automated methods into diagnostic processes, promising enhanced accuracy and efficacy in healthcare diagnostics.


Spring 2024

Course Name

STA 6367 Data Science 2

Instructor Name

Dr. Rui Xie


College of Sciences

Accessibility Status

PDF accessibility verified using Adobe Acrobat Pro Accessibility Checker

Included in

Data Science Commons