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
Main Authors:	Ahmed, Kazi sabab, Shariar, Khandaker Sadab, Naim, Naimul Hasan, Hazari, MD. Nayimur Rahman
Outros autores:	Alam, Md. Golam Rabiul
Formato:	Thesis
Idioma:	English
Publicado:	Brac University 2022
Subjects:	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 liña:	http://hdl.handle.net/10361/17333

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

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