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Intracranial Hemorrhage Detection using Machine Learning Models

Patro, Bellana Tirupati (2020) Intracranial Hemorrhage Detection using Machine Learning Models. Masters thesis, Dublin, National College of Ireland.

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Intracranial Hemorrhage is the internal bleeding caused in the skull and brain due to excessive intake of alcohol, stoke,etc. Radiologists sometimes miss subtle and critical findings which when detected at later stages becomes a critical concern. Previous research on this field mainly focused on feature extraction and image classification using supervised learning which produces inaccurate results due to inappropriate input data. This research work concentrates on modelling a network for detection of intracranial hemorrhage using sparse autoencoder for unsupervised machine learning for complex feature extraction then feeding to SVM classifier for Image Classification. This deep learning network was compared with other models like Random Forest, CNNs, PCA with a SVM classifier. The performance shown by sparse autoencoder with SVM classifier was better than other models.

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
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 25 Jan 2021 14:07
Last Modified: 25 Jan 2021 14:07

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