Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease.
Recently, the transfer learning technique has proved to be powerful in enhancing the development of deep learning methods for sickle cell disease (SCD) detection as a complement to the clinical method where a hemoglobin electrophoresis machine is used. This is evidenced by some models and algorithms...
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| Format: | Article |
| Language: | en_US |
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Kabale University
2024
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| Online Access: | http://hdl.handle.net/20.500.12493/2001 |
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| author | Mabirizi, Vicent Kawuma, Simon Kyarisiima, Addah Bamutura, David Atwiine, Barnabas Nanjebe, Deborah Oyesigye, Adolf Mukama |
| author_facet | Mabirizi, Vicent Kawuma, Simon Kyarisiima, Addah Bamutura, David Atwiine, Barnabas Nanjebe, Deborah Oyesigye, Adolf Mukama |
| author_sort | Mabirizi, Vicent |
| collection | KAB-DR |
| description | Recently, the transfer learning technique has proved to be powerful in enhancing the development of deep learning methods for sickle cell disease (SCD) detection as a complement to the clinical method where a hemoglobin electrophoresis machine is used. This is evidenced by some models and algorithms with ≥90% prediction accuracy. From the literature, most of the proposed methods are trained and tested on pre-trained deep learning models like VGG16, VGG19, ResNet, Inception_V3, and ReNet. However, training and testing of these methods are limited to one model and separate datasets which may lead to biased results due to implementation in a variation of these models which affects the results produced. To this end, there exists a need to evaluate the SCD models using the same dataset. Thus, in this research study, we carried out a comparative investigation and evaluated predominate pre-trained models used to detect SCD using the same dataset to ascertain which one has the best accuracy. We used a secondary dataset obtained from an online dataset. In our study, we have discovered that Inception V3 yielded the highest accuracy of 97.3% followed by VGG19 at 97.0%, VGG16 at 91%, ResNet50 at 82% and ReNet at 67%, and the CNN-scratch model achieved 81% accuracy. Results from our study will aid researchers and industry practitioners in making decisions on the best deep-learning model to use while detecting SCD. |
| format | Article |
| id | oai:idr.kab.ac.ug:20.500.12493-2001 |
| institution | KAB-DR |
| language | en_US |
| publishDate | 2024 |
| publisher | Kabale University |
| record_format | dspace |
| spelling | oai:idr.kab.ac.ug:20.500.12493-20012024-08-01T00:01:18Z Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. Mabirizi, Vicent Kawuma, Simon Kyarisiima, Addah Bamutura, David Atwiine, Barnabas Nanjebe, Deborah Oyesigye, Adolf Mukama Deep Learning Techniques Models Sickle Cell Disease Detection Recently, the transfer learning technique has proved to be powerful in enhancing the development of deep learning methods for sickle cell disease (SCD) detection as a complement to the clinical method where a hemoglobin electrophoresis machine is used. This is evidenced by some models and algorithms with ≥90% prediction accuracy. From the literature, most of the proposed methods are trained and tested on pre-trained deep learning models like VGG16, VGG19, ResNet, Inception_V3, and ReNet. However, training and testing of these methods are limited to one model and separate datasets which may lead to biased results due to implementation in a variation of these models which affects the results produced. To this end, there exists a need to evaluate the SCD models using the same dataset. Thus, in this research study, we carried out a comparative investigation and evaluated predominate pre-trained models used to detect SCD using the same dataset to ascertain which one has the best accuracy. We used a secondary dataset obtained from an online dataset. In our study, we have discovered that Inception V3 yielded the highest accuracy of 97.3% followed by VGG19 at 97.0%, VGG16 at 91%, ResNet50 at 82% and ReNet at 67%, and the CNN-scratch model achieved 81% accuracy. Results from our study will aid researchers and industry practitioners in making decisions on the best deep-learning model to use while detecting SCD. 2024-05-27T14:29:34Z 2024-05-27T14:29:34Z 2023 Article Mabirizi, V. et al. (2023). Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. Kabale: Kabale University. http://hdl.handle.net/20.500.12493/2001 en_US application/pdf Kabale University |
| spellingShingle | Deep Learning Techniques Models Sickle Cell Disease Detection Mabirizi, Vicent Kawuma, Simon Kyarisiima, Addah Bamutura, David Atwiine, Barnabas Nanjebe, Deborah Oyesigye, Adolf Mukama Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. |
| title | Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. |
| title_full | Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. |
| title_fullStr | Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. |
| title_full_unstemmed | Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. |
| title_short | Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. |
| title_sort | comparison of deep learning techniques in detection of sickle cell disease |
| topic | Deep Learning Techniques Models Sickle Cell Disease Detection |
| url | http://hdl.handle.net/20.500.12493/2001 |
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