Intracranial Hemorrhage Classification From CT Scan Using Deep Learning and Bayesian Optimization
Intracranial hemorrhage is a medical condition characterized by bleeding within the skull or brain tissue. It has mainly five subtypes: epidural, subdural, subarachnoid, intraparenchymal, and intraventricular. To ensure a successful outcome for a patient, timely and accurate identification of intrac...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10198438/ |
| _version_ | 1797743425059028992 |
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| author | Shifat E. Arman Sayed Saminur Rahman Niloy Irtisam Shamim Ahmed Deowan Md. Ariful Islam Saadman Sakib Mehedi Hasan |
| author_facet | Shifat E. Arman Sayed Saminur Rahman Niloy Irtisam Shamim Ahmed Deowan Md. Ariful Islam Saadman Sakib Mehedi Hasan |
| author_sort | Shifat E. Arman |
| collection | DOAJ |
| description | Intracranial hemorrhage is a medical condition characterized by bleeding within the skull or brain tissue. It has mainly five subtypes: epidural, subdural, subarachnoid, intraparenchymal, and intraventricular. To ensure a successful outcome for a patient, timely and accurate identification of intracranial hemorrhage is crucial. However, a shortage of radiologists, particularly in rural areas, can lead to a delay in diagnosis. In this work, we proposed an automatic way of identifying intracranial hemorrhage from a Computed Tomography (CT) scan. To classify intracranial hemorrhage accurately, we have optimized the Densely Connected Convolutional Network (DenseNet) using Bayesian Optimization (BO). We utilized Bayesian optimization (BO) to determine the optimal learning rate, optimizer, and the number of nodes in the dense layer for the DenseNet architecture. Our proposed model can analyze CT scans to detect the presence of hemorrhage and identify its subtype. The optimized DenseNet model showcased remarkable performance. By ensuring accurate and reliable diagnoses, our method will assist doctors in making better-informed decisions and providing better care for their patients. |
| first_indexed | 2024-03-12T14:55:18Z |
| format | Article |
| id | doaj.art-13b75349c43d4ae6907dd175c6e945fa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:55:18Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-13b75349c43d4ae6907dd175c6e945fa2023-08-14T23:00:22ZengIEEEIEEE Access2169-35362023-01-0111834468346010.1109/ACCESS.2023.330077110198438Intracranial Hemorrhage Classification From CT Scan Using Deep Learning and Bayesian OptimizationShifat E. Arman0https://orcid.org/0000-0002-3636-1444Sayed Saminur Rahman1Niloy Irtisam2https://orcid.org/0009-0000-0165-6165Shamim Ahmed Deowan3https://orcid.org/0000-0003-1453-9843Md. Ariful Islam4Saadman Sakib5https://orcid.org/0009-0005-0080-1451Mehedi Hasan6https://orcid.org/0009-0002-0194-4575Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, BangladeshAromo Health, Chattogram, BangladeshDepartment of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, BangladeshDepartment of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, BangladeshDepartment of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, BangladeshDepartment of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, BangladeshDepartment of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, BangladeshIntracranial hemorrhage is a medical condition characterized by bleeding within the skull or brain tissue. It has mainly five subtypes: epidural, subdural, subarachnoid, intraparenchymal, and intraventricular. To ensure a successful outcome for a patient, timely and accurate identification of intracranial hemorrhage is crucial. However, a shortage of radiologists, particularly in rural areas, can lead to a delay in diagnosis. In this work, we proposed an automatic way of identifying intracranial hemorrhage from a Computed Tomography (CT) scan. To classify intracranial hemorrhage accurately, we have optimized the Densely Connected Convolutional Network (DenseNet) using Bayesian Optimization (BO). We utilized Bayesian optimization (BO) to determine the optimal learning rate, optimizer, and the number of nodes in the dense layer for the DenseNet architecture. Our proposed model can analyze CT scans to detect the presence of hemorrhage and identify its subtype. The optimized DenseNet model showcased remarkable performance. By ensuring accurate and reliable diagnoses, our method will assist doctors in making better-informed decisions and providing better care for their patients.https://ieeexplore.ieee.org/document/10198438/Bayesian optimizationCT scandeep learningintracranial hemorrhagemedical image analysisradiology |
| spellingShingle | Shifat E. Arman Sayed Saminur Rahman Niloy Irtisam Shamim Ahmed Deowan Md. Ariful Islam Saadman Sakib Mehedi Hasan Intracranial Hemorrhage Classification From CT Scan Using Deep Learning and Bayesian Optimization IEEE Access Bayesian optimization CT scan deep learning intracranial hemorrhage medical image analysis radiology |
| title | Intracranial Hemorrhage Classification From CT Scan Using Deep Learning and Bayesian Optimization |
| title_full | Intracranial Hemorrhage Classification From CT Scan Using Deep Learning and Bayesian Optimization |
| title_fullStr | Intracranial Hemorrhage Classification From CT Scan Using Deep Learning and Bayesian Optimization |
| title_full_unstemmed | Intracranial Hemorrhage Classification From CT Scan Using Deep Learning and Bayesian Optimization |
| title_short | Intracranial Hemorrhage Classification From CT Scan Using Deep Learning and Bayesian Optimization |
| title_sort | intracranial hemorrhage classification from ct scan using deep learning and bayesian optimization |
| topic | Bayesian optimization CT scan deep learning intracranial hemorrhage medical image analysis radiology |
| url | https://ieeexplore.ieee.org/document/10198438/ |
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