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.

Detalles Bibliográficos
Autores principales: Ahmed, Kazi sabab, Shariar, Khandaker Sadab, Naim, Naimul Hasan, Hazari, MD. Nayimur Rahman
Otros Autores: Alam, Md. Golam Rabiul
Formato: Tesis
Lenguaje:English
Publicado: Brac University 2022
Materias:
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
Acceso en línea:http://hdl.handle.net/10361/17333

Internet

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

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