Sickle Cell Anaemia Detection using Deep Learning | ||||
| International Journal of Artificial Intelligence and Emerging Technology | ||||
| Volume 6, Issue 1, June 2023, Page 15-26 PDF (553.33 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijaiet.2024.240910.1002 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
shereen A. Hussien   1; Mostafa A. Amer2; Doaa Ibrahim2
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| 1computer science, faculty of computers and artificial intelligence, fayoum university | ||||
| 2Bioinformatics, faculty of computers and artificial intelligence, fayoum university | ||||
| Abstract | ||||
| Many disorders, including sickle cell anaemia which causes periodic bouts of pain and severe, pronounced anaemia, result in red blood cell (RBC) deformation. It takes longer to monitor patients with these disorders since peripheral blood samples must be examined under a microscope. The observation of isolated RBCs is subjective; hence the error rate is considerable and an expert is needed to perform this approach, SCD can be adequately managed and the death rate can be decreased with early detection. Therefore, this work proposes a deep learning method for sickle cell detection based on Convolutional neural networks (CNN). VGG model differentiate between three classes of red blood cells which are circular (normal), elongated (sickle cells), and other blood content. it is applied on ERYTHROCYTESIDB dataset for validation. A comparison of the results showed that the proposed model is superior for the diagnosis of sickle cell anaemia with 99.4% of overall accuracy. | ||||
| Keywords | ||||
| Sickle Cell Anaemia; Red Blood Cell; CNN; VGG; Transfer Learning | ||||
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