Detection of intracranial hemorrhage on CT scan images using convolutional neural network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| التنسيق: | أطروحة |
| اللغة: | English |
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Brac University
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/15890 |
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10361-15890 |
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10361-158902022-01-26T10:13:12Z Detection of intracranial hemorrhage on CT scan images using convolutional neural network Rahman, Afridi Ibn Bhuiyan, Subhi Reza, Ziad Hasan Zaheen, Jasarat Khan, Tasin Al Nahian Parvez, Mohammad Zavid Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University Deep learning Convolutional neural network CT Scan Images Intracranial hemorrhage Efficient NetB6 DenseNet121 ResNet50 InceptionResNetV2 InceptionV3 VGG16 Artificial neural networks Machine learning. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-38). Intracranial hemorrhage is an acute bleeding within the skull which can damage the brain tissue and can eventually lead to disability or even death. It is a serious medical condition that occurs when blood is built up within the skull after a blood vessel is ruptured. Brain damage can be minimized if intracranial hemorrhage is diagnosed immediately, and the patient may regain mobility. Deploying applications of Artificial Intelligence (AI) in clinical medicine to accelerate the accuracy of intracranial hemorrhage diagnosis aims to minimize the severity of the condition, therefore, enhancing medical care. Adequate analysis of the Computed Tomography (CT) scan imaging is integral for diagnosis and management. Deep Learning, which is a subset of AI, is widely used in interpreting medical images and has shown promising advancements in diagnosing brain hemorrhage. With time playing a crucial factor, automatic lesion identification is one of the most important factors in precision medicine dealing with huge datasets of neuroimaging compared to manual lesion segmentation. This paper proposes a Deep Learning method called Convolutional Neural Network (CNN) on neuroimaging with transfer learning techniques to assist in the diagnosis of intracranial hemorrhage on CT scan images. We used six pretrained CNN models (EfficientNetB6, DenseNet121, ResNet50, InceptionRes- NetV2, InceptionV3, VGG16) and also present a traditional 11-layer CNN model for binary classification and detection of intracranial hemorrhage using brain CT scan images. The paper depicts a comparative analysis on the performance between the proposed traditional and pre-trained CNN models in terms of accuracy, precision, recall, F1 score, and AUC curve on the existing dataset. The EfficientNetB6 model yields an accuracy of 95.99%, which is higher than any of the experimental results of the CNN models used in this dataset. Afridi Ibn Rahman Subhi Bhuiyan Ziad Hasan Reza Jasarat Zaheen Tasin Al Nahian Khan B. Computer Science 2022-01-13T04:08:20Z 2022-01-13T04:08:20Z 2021 2021-09 Thesis ID 17201107 ID 17201116 ID 17201076 ID 17201100 ID 17201085 http://hdl.handle.net/10361/15890 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. 38 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Deep learning Convolutional neural network CT Scan Images Intracranial hemorrhage Efficient NetB6 DenseNet121 ResNet50 InceptionResNetV2 InceptionV3 VGG16 Artificial neural networks Machine learning. |
| spellingShingle |
Deep learning Convolutional neural network CT Scan Images Intracranial hemorrhage Efficient NetB6 DenseNet121 ResNet50 InceptionResNetV2 InceptionV3 VGG16 Artificial neural networks Machine learning. Rahman, Afridi Ibn Bhuiyan, Subhi Reza, Ziad Hasan Zaheen, Jasarat Khan, Tasin Al Nahian Detection of intracranial hemorrhage on CT scan images using convolutional neural network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Parvez, Mohammad Zavid |
| author_facet |
Parvez, Mohammad Zavid Rahman, Afridi Ibn Bhuiyan, Subhi Reza, Ziad Hasan Zaheen, Jasarat Khan, Tasin Al Nahian |
| format |
Thesis |
| author |
Rahman, Afridi Ibn Bhuiyan, Subhi Reza, Ziad Hasan Zaheen, Jasarat Khan, Tasin Al Nahian |
| author_sort |
Rahman, Afridi Ibn |
| title |
Detection of intracranial hemorrhage on CT scan images using convolutional neural network |
| title_short |
Detection of intracranial hemorrhage on CT scan images using convolutional neural network |
| title_full |
Detection of intracranial hemorrhage on CT scan images using convolutional neural network |
| title_fullStr |
Detection of intracranial hemorrhage on CT scan images using convolutional neural network |
| title_full_unstemmed |
Detection of intracranial hemorrhage on CT scan images using convolutional neural network |
| title_sort |
detection of intracranial hemorrhage on ct scan images using convolutional neural network |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/15890 |
| work_keys_str_mv |
AT rahmanafridiibn detectionofintracranialhemorrhageonctscanimagesusingconvolutionalneuralnetwork AT bhuiyansubhi detectionofintracranialhemorrhageonctscanimagesusingconvolutionalneuralnetwork AT rezaziadhasan detectionofintracranialhemorrhageonctscanimagesusingconvolutionalneuralnetwork AT zaheenjasarat detectionofintracranialhemorrhageonctscanimagesusingconvolutionalneuralnetwork AT khantasinalnahian detectionofintracranialhemorrhageonctscanimagesusingconvolutionalneuralnetwork |
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