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.

Bibliographische Detailangaben
Hauptverfasser:	Ahmed, Kazi sabab, Shariar, Khandaker Sadab, Naim, Naimul Hasan, Hazari, MD. Nayimur Rahman
Weitere Verfasser:	Alam, Md. Golam Rabiul
Format:	Abschlussarbeit
Sprache:	English
Veröffentlicht:	Brac University 2022
Schlagworte:	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 Zugang:	http://hdl.handle.net/10361/17333

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

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