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.
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Brac University
2022
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| 在线阅读: | http://hdl.handle.net/10361/17333 |
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10361-173332022-09-27T21:02:16Z Intracranial hemorrhage detection using CNN-LSTM fusion model Ahmed, Kazi sabab Shariar, Khandaker Sadab Naim, Naimul Hasan Hazari, MD. Nayimur Rahman Alam, Md. Golam Rabiul Shoumo, Syed Zamil Hasan Department of Computer Science and Engineering, Brac University 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) Cognitive learning theory (Deep learning) Neural networks (Computer science) Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-43). Intracranial Hemorrhage is a term used to describe bleeding between the brain tissue and the skull or within the brain tissue itself. It is life-threatening and needs immediate medical attention. As the first response, it is indispensable to detect the type of intracranial hemorrhage as soon as possible. Now, the manual detection methods require the help of an imaging expert and are certainly very time-consuming. Although there are several techniques for identifying them such as utilizing CT-scan images, magnetic resonance imaging (MRI), magnetic resonance angiogram (MRA), and ultrasound-based images, the results are still not adequate and have much room for improvement. In addition to these methods, researchers have also used imaging strategies based on Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) for this purpose. Therefore, this research aims to combine both these two fields and propose a model based on Deep Learning(DL) to detect intracranial hemorrhage. The goal of this paper is to automate the detection of intracranial hemorrhage and make the process more efficient and accurate. The model is expected to provide us with satisfactory results and can be used as an effective alternative to the existing methods. Kazi sabab Ahmed Khandaker Sadab Shariar Naimul Hasan Naim MD. Nayimur Rahman Hazari B. Computer Science and Engineering 2022-09-27T04:59:11Z 2022-09-27T04:59:11Z 2022 2022-05 Thesis ID 18101509 ID 18101306 ID 18301192 ID 18101667 http://hdl.handle.net/10361/17333 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 43 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
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) Cognitive learning theory (Deep learning) Neural networks (Computer science) Machine learning |
| spellingShingle |
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) Cognitive learning theory (Deep learning) Neural networks (Computer science) Machine learning Ahmed, Kazi sabab Shariar, Khandaker Sadab Naim, Naimul Hasan Hazari, MD. Nayimur Rahman Intracranial hemorrhage detection using CNN-LSTM fusion model |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Ahmed, Kazi sabab Shariar, Khandaker Sadab Naim, Naimul Hasan Hazari, MD. Nayimur Rahman |
| format |
Thesis |
| author |
Ahmed, Kazi sabab Shariar, Khandaker Sadab Naim, Naimul Hasan Hazari, MD. Nayimur Rahman |
| author_sort |
Ahmed, Kazi sabab |
| title |
Intracranial hemorrhage detection using CNN-LSTM fusion model |
| title_short |
Intracranial hemorrhage detection using CNN-LSTM fusion model |
| title_full |
Intracranial hemorrhage detection using CNN-LSTM fusion model |
| title_fullStr |
Intracranial hemorrhage detection using CNN-LSTM fusion model |
| title_full_unstemmed |
Intracranial hemorrhage detection using CNN-LSTM fusion model |
| title_sort |
intracranial hemorrhage detection using cnn-lstm fusion model |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17333 |
| work_keys_str_mv |
AT ahmedkazisabab intracranialhemorrhagedetectionusingcnnlstmfusionmodel AT shariarkhandakersadab intracranialhemorrhagedetectionusingcnnlstmfusionmodel AT naimnaimulhasan intracranialhemorrhagedetectionusingcnnlstmfusionmodel AT hazarimdnayimurrahman intracranialhemorrhagedetectionusingcnnlstmfusionmodel |
| _version_ |
1814307732114636800 |