Date of Award

2020

Publisher

North Dakota State University

Document Type

Master's Paper

Degree Awarded

Master of Science (MS)

Program

Computer Science

Department

Computer Science

College

Engineering

Faculty Advisor

Nygard, Kendall

Description

An estimate of about 700,000 Americans today live with a brain tumor. Of these, 70% are benign and 30% are malicious. The average survival rate of all the malicious brain tumor patients is 35%. Diagnosing these tumors early on gives the best chance for survival. The Doctors use MRI scans to identify the presence of a tumor and it’s characteristics like the type and size. In this paper, I implemented a Deep learning convolutional neural network model that classifies the brain tumors using MRI scans. We shall use VGG-16 deep-learning approach to implement the machine learning algorithm. The proposed system can be divided into 3 parts: data input and preprocessing, building the VGG-16 model, image classification using the built model. Using this approach, I have achieved 80% accuracy. The accuracy of the model developed will depend on how correctly the affected brain tumor images can be classified from the unaffected.

Handle Identifier

https://hdl.handle.net/10365/31366

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