An interpretable transformer based approach to classify Malaria from blood cell images
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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Brac University
2023
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| גישה מקוונת: | http://hdl.handle.net/10361/20000 |
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10361-200002023-08-28T06:31:43Z An interpretable transformer based approach to classify Malaria from blood cell images Islam, Mehafuza Al Mamun, S.M. Abdulla Alam, Dr. Md. Ashraful Department of Computer Science and Engineering, Brac University Malaria parasites Vision transformer Deep learning Grad-CAM Cognitive learning theory (Deep learning) Artificial intelligence Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-32). Malaria is a disease that can be fatal, and it is spread through the bite of the female Anopheles mosquito. The life of the sufferer is put in jeopardy as a result of the pres ence of numerous plasmodium parasites, which spread throughout their blood cells. Malaria can potentially be fatal if it is not treated within the first few stages of the disease. A well-known method for diagnosing malaria, microscopy involves taking blood samples from the patient, calculating the number of parasites, and counting the victim’s red blood cells. Nevertheless, the procedure of microscopy takes a lot of time, and, in certain circumstances, it can produce an incorrect result. When com pared to the more conventional approach of microscopic examination, the recent successes of deep learning (DL) in the field of medical diagnosis make it quite con ceivable to reduce the expenses associated with the diagnosis while simultaneously improving overall detection accuracy. This study proposes a transformer-based DL technique for diagnosing the malaria parasite using blood cell images. An explain able AI technique called Grad-CAM was applied in order to determine which aspects of an image the proposed model paid significantly more attention to in comparison to the other aspects of the image through saliency mapping. This was done in or der to demonstrate the usefulness of the models. According to the findings of this research, the performance of the vision transformer and the vgg-16 are identical. Both models have reached an accuracy score of approximately 96%, which is very impressive. Mehafuza Islam S.M. Abdulla Al Mamun B. Computer Science and Engineering 2023-08-27T09:55:29Z 2023-08-27T09:55:29Z 2023 2023-01 Thesis ID: 17301228 ID: 17301031 http://hdl.handle.net/10361/20000 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. 32 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Malaria parasites Vision transformer Deep learning Grad-CAM Cognitive learning theory (Deep learning) Artificial intelligence Machine learning |
| spellingShingle |
Malaria parasites Vision transformer Deep learning Grad-CAM Cognitive learning theory (Deep learning) Artificial intelligence Machine learning Islam, Mehafuza Al Mamun, S.M. Abdulla An interpretable transformer based approach to classify Malaria from blood cell images |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Alam, Dr. Md. Ashraful |
| author_facet |
Alam, Dr. Md. Ashraful Islam, Mehafuza Al Mamun, S.M. Abdulla |
| format |
Thesis |
| author |
Islam, Mehafuza Al Mamun, S.M. Abdulla |
| author_sort |
Islam, Mehafuza |
| title |
An interpretable transformer based approach to classify Malaria from blood cell images |
| title_short |
An interpretable transformer based approach to classify Malaria from blood cell images |
| title_full |
An interpretable transformer based approach to classify Malaria from blood cell images |
| title_fullStr |
An interpretable transformer based approach to classify Malaria from blood cell images |
| title_full_unstemmed |
An interpretable transformer based approach to classify Malaria from blood cell images |
| title_sort |
interpretable transformer based approach to classify malaria from blood cell images |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/20000 |
| work_keys_str_mv |
AT islammehafuza aninterpretabletransformerbasedapproachtoclassifymalariafrombloodcellimages AT almamunsmabdulla aninterpretabletransformerbasedapproachtoclassifymalariafrombloodcellimages AT islammehafuza interpretabletransformerbasedapproachtoclassifymalariafrombloodcellimages AT almamunsmabdulla interpretabletransformerbasedapproachtoclassifymalariafrombloodcellimages |
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1814307943086030848 |