Brain Tumor Detection using Deep Learning Models

Sawant, Nehal Deepak (2022) Brain Tumor Detection using Deep Learning Models. Masters thesis, Dublin, National College of Ireland.

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Abstract

Brain tumors are made up of abnormal brain cells. Brain cancer is classified into benign and malignant tumors. Most tumors are diagnosed using magnetic resonance imaging (MRI). The early detection of a brain tumor is crucial, since it is a fatal condition. This study, therefore, employs Inception V3, VGG-16, and ResNet50 models, which are deep learning and transfer learning models, respectively. In this study, data augmentation is proposed to minimize overfitting since limited MRI data were used in the project. The study will use hyper-parameter tuning in order to provide field workers with a more accurate model. Critically evaluated metrics such as accuracy, precision and recall are used.In this study, VGG16, InceptionV3 and ResNet50 gives accuracy of 75%,67% and 94%. With the ResNet50 giving better accuracy, it can efficetively detect brain tumor hence healthcare workers can provide better treatment.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > Biomedical engineering Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Q Science > Life sciences > Medical sciences > Pathology > Tumors
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	10 Mar 2023 17:05
Last Modified:	10 Mar 2023 17:05
URI:	https://norma.ncirl.ie/id/eprint/6295

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