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Brain Tumor Segmentation Using Convolutional Neural Network

Chheda, Smit Jagdish (2020) Brain Tumor Segmentation Using Convolutional Neural Network. Masters thesis, Dublin, National College of Ireland.

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Segmentation of the brain tumors is an essential function in the production of clinical data. Early care plays an important part in enhancing patient safety and increasing patient sustenance rates. The purpose of this implementation is to find out how does Neural network models results out when trained on large data set of MRIs. A novel method to image segmentation was applied to separate tumor from magnetic resonance (MRI) images by testing Neural network models- UNet, Feature Pyramid Network and ResNet50.Data set contained a total of 3886 MRI that were used to implement the approach where at first, augmentation was done and then the models were trained on large data sets after which tumor were segmented. For evaluating which model performed better, Dice co-efficient and IoU were used and the same were the best for ResNet50 with values 0.91 and 90% respectively. Its loss value came out to be 0.16 which is quite minimal. To conclude, segmentation is possible with good accuracy on large data sets by using ResNet50 and other neural network models. For future, I plan to implement the classification of tumors for the surgeons or the doctors to know whether the tumor is dangerous or mild. This can make it easy for them to know what surgery is needed to maintain patient’s safety.
Keywords: Tumor Segmentation, Neural Networks, ResNet50, Unet, FPN

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
R Medicine > R Medicine (General)
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 20 Jan 2021 13:33
Last Modified: 20 Jan 2021 13:33

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