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.

Bibliografische gegevens
Hoofdauteurs: Ahmed, Kazi sabab, Shariar, Khandaker Sadab, Naim, Naimul Hasan, Hazari, MD. Nayimur Rahman
Andere auteurs: Alam, Md. Golam Rabiul
Formaat: Thesis
Taal:English
Gepubliceerd in: Brac University 2022
Onderwerpen:
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
Online toegang:http://hdl.handle.net/10361/17333

Internet

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

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