Intracranial hemorrhage detection using CNN-LSTM fusion model

Intracranial hemorrhage detection using CNN-LSTM fusion model

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Opis bibliograficzny
Główni autorzy: Ahmed, Kazi sabab, Shariar, Khandaker Sadab, Naim, Naimul Hasan, Hazari, MD. Nayimur Rahman
Kolejni autorzy: Alam, Md. Golam Rabiul
Format: Praca dyplomowa
Język:English
Wydane: Brac University 2022
Hasła przedmiotowe:
Deep learning
Convolutional neural network (CNN)
Magnetic Resonance Imaging (MRI)
Magnetic Resonance Angiogram (MRA)
Intracranial hemorrhage
Recurrent Neural Network (RNN)
Cognitive learning theory (Deep learning)
Neural networks (Computer science)
Machine learning
Dostęp online:http://hdl.handle.net/10361/17333

Internet

http://hdl.handle.net/10361/17333

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