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Intracranial Hemorrhage Detection Using Deep Learning and Transfer Learning

Kumar, Rohit (2021) Intracranial Hemorrhage Detection Using Deep Learning and Transfer Learning. Masters thesis, Dublin, National College of Ireland.

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Intracranial Hemorrhage (ICH) is a life-threatening medical occurrence that is associated with a poor result despite optimal care. Given that early detection and care of ICH can improve health outcomes, there is a need for a triage system that can promptly detect and speed the treatment process. Previously published work took a more traditional technique, comprising numerous steps of alignment, image analysis, image rectification, handmade image segmentation, and classification. This research work examines the intracranial hemorrhage detection problem and develops a deep learning model and transfer learning models to reduce the time required to identify hemorrhages. For classification of ICH sub types, we developed a convolutional neural network based on the Transfer learning Model. DenseNet121, Xception and CNNs were compared with using many evaluation criteria to ensure that the model’s results are accurate and that it does an excellent job. As predicted, the system delivers impressive results, and the data reveal that Xception is more successful than competing models. For the identification and classification of ICH subtypes, the Xception model is used for the final output.

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)
H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 06 Dec 2021 13:20
Last Modified: 06 Dec 2021 13:20

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