Date of Award


Document Type


Degree Name

Master of Science in Computer Science


School of Computer Science and Engineering

First Reader/Committee Chair

Yan, Zhang


Brain Tumors are abnormal growth of cells within the brain that can be categorized as benign (non-cancerous) or malignant (cancerous). Accurate and timely classification of brain tumors is crucial for effective treatment planning and patient care. Medical imaging techniques like Magnetic Resonance Imaging (MRI) provide detailed visualizations of brain structures, aiding in diagnosis and tumor classification[8].

In this project, we propose a brain tumor classifier applying deep learning methodologies to automatically classify brain tumor images without any manual intervention. The classifier uses deep learning architectures to extract and classify brain MRI images. Specifically, a Convolutional Neural Network (CNN) is trained on a diverse dataset of brain tumor images. The CNN learns intricate patterns and features within the images, enabling it to classify various tumor types. Transfer learning, utilizing pre-trained models such as Visual Geometry Group (VGG) and EfficientNet (B3), enhances the CNN model's ability to generalize across different datasets. Additionally, a traditional neural network Multi-Layer Perceptron (MLP) is applied to classify brain MRI images.

The performance of the VGG, EfficientNet, and MLP models are evaluated and compared. The metrics of accuracy, precision, recall, and F1 score are used to evaluate the efficacy of each model in brain tumor classification. This project contributes to the advancement of automated brain tumors tumor diagnosis, potentially improving patient outcomes through more efficient diagnosis strategies.