Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease.

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dc.contributor.authorMabirizi, Vicent
dc.contributor.authorKawuma, Simon
dc.contributor.authorKyarisiima, Addah
dc.contributor.authorBamutura, David
dc.contributor.authorAtwiine, Barnabas
dc.contributor.authorNanjebe, Deborah
dc.contributor.authorOyesigye, Adolf Mukama
dc.date.accessioned2024-05-27T14:29:34Z
dc.date.available2024-05-27T14:29:34Z
dc.date.issued2023
dc.description.abstractRecently, 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.
dc.identifier.citationMabirizi, V. et al. (2023). Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. Kabale: Kabale University.
dc.identifier.urihttp://hdl.handle.net/20.500.12493/2001
dc.language.isoen_US
dc.publisherKabale University
dc.subjectDeep Learning
dc.subjectTechniques
dc.subjectModels
dc.subjectSickle Cell Disease
dc.subjectDetection
dc.titleComparison of Deep Learning Techniques in Detection of Sickle Cell Disease.
dc.typeArticle

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