| dc.contributor.author | Thotapally, Shanthanreddy | |
| dc.description.abstract | 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. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.title | Brain Cancer Detection Using MRI Scans | en_US |
| dc.type | Master's paper | en_US |
| dc.date.accessioned | 2020-01-21T20:53:55Z | |
| dc.date.available | 2020-01-21T20:53:55Z | |
| dc.date.issued | 2020 | en_US |
| dc.identifier.uri | https://hdl.handle.net/10365/31366 | |
| dc.subject.lcsh | Brain -- Magnetic resonance imaging. | |
| dc.subject.lcsh | Brain -- Tumors -- Diagnosis. | |
| dc.subject.lcsh | Machine learning. | |
| dc.subject.lcsh | Algorithms. | |
| dc.subject.lcsh | Neural networks (Computer science) | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Nygard, Kendall | |
