Performance comparison of CNN architectures for detecting Malaria diseases
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
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| Язык: | English |
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Brac University
2021
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| Online-ссылка: | http://hdl.handle.net/10361/14725 |
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10361-147252022-01-26T10:23:14Z Performance comparison of CNN architectures for detecting Malaria diseases Rinky, Habiba Karim Bhuiyan, Rakeya Rahim Rahman, Humayra Tasnim Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Malaria Plasmodium falciparum Plasmodium vivax Plasmodium malaria Plasmodium ovale Deep learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 38-39). World is facing an acute health crisis for the disease named malaria caused by the bite of female mosquitoes of parasite named genus plasmodium. From different research of all time it is clear that this disease is not confined within a certain specific region or area rather this infection is common all over the world. Many researchers from all over the globe discovered many processes or techniques to determine malaria infection from host body. Malaria Detection consumes huge time to detect. This study aims to determine the infected malaria cells using deep learning algorithms as it is important part in this advanced technological era to determine objects. This research used deep learning algorithms like VGG-16, VGG-19, VGG-16 binary, VGG-19 binary, Alexnet, MobileNet, ResNet34, ResNet50 and CNN2D to determine malaria infected cells from images. Thereby, also finds the comparative analysis between these algorithms to determine the best accuracy giving algorithm. From the study it is evident that algorithms named AlexNet, VGG-16, VGG-19, VGG- 16 binary, VGG-19 binary, MobileNet, CNN2D, ResNet34 and ResNet50 give an accuracy of 94.84%, 92%, 92%, 97.4%, 96.53%, 95.42%, 96.91%, 97.06% and 85% respectively. From the comparative analysis between these nine algorithms, this study concludes to and ResNet34 with model accuracy 97.06% as the best accuracy giving algorithm. Habiba Karim Rinky Rakeya Rahim Bhuiyan Humayra Tasnim Rahman B. Computer Science 2021-07-03T14:00:49Z 2021-07-03T14:00:49Z 2020 2020-04 Thesis ID 16101252 ID 16101063 ID 16101127 http://hdl.handle.net/10361/14725 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. 39 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Malaria Plasmodium falciparum Plasmodium vivax Plasmodium malaria Plasmodium ovale Deep learning |
| spellingShingle |
Malaria Plasmodium falciparum Plasmodium vivax Plasmodium malaria Plasmodium ovale Deep learning Rinky, Habiba Karim Bhuiyan, Rakeya Rahim Rahman, Humayra Tasnim Performance comparison of CNN architectures for detecting Malaria diseases |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Rinky, Habiba Karim Bhuiyan, Rakeya Rahim Rahman, Humayra Tasnim |
| format |
Thesis |
| author |
Rinky, Habiba Karim Bhuiyan, Rakeya Rahim Rahman, Humayra Tasnim |
| author_sort |
Rinky, Habiba Karim |
| title |
Performance comparison of CNN architectures for detecting Malaria diseases |
| title_short |
Performance comparison of CNN architectures for detecting Malaria diseases |
| title_full |
Performance comparison of CNN architectures for detecting Malaria diseases |
| title_fullStr |
Performance comparison of CNN architectures for detecting Malaria diseases |
| title_full_unstemmed |
Performance comparison of CNN architectures for detecting Malaria diseases |
| title_sort |
performance comparison of cnn architectures for detecting malaria diseases |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14725 |
| work_keys_str_mv |
AT rinkyhabibakarim performancecomparisonofcnnarchitecturesfordetectingmalariadiseases AT bhuiyanrakeyarahim performancecomparisonofcnnarchitecturesfordetectingmalariadiseases AT rahmanhumayratasnim performancecomparisonofcnnarchitecturesfordetectingmalariadiseases |
| _version_ |
1814309635367108608 |