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Real-Time Sickle Cell Anemia Diagnosis Based Hardware Accelerator

Part of the Communications in Computer and Information Science book series (CCIS,volume 1183)


Sickle cell anemia (SCA) is a blood disease, which causes distortion in the shape of Red Blood Cells (RBCs) and becomes like a crescent. Traditional methodologies of classifying and counting RBCs that have been used by medical analysts are time-consuming, as well as, cost-effective. In addition, it is possible to make errors throughout the classifying and counting stages. We overcome these limitations by proposing a novel convolutional neural network model that classifies the RBCs into three categories: Normal, Abnormal (sickle shape) and other blood content. We enhanced the model’s efficiency using a hardware accelerator called FPGA (Altera DE2 Cyclone II) to take advantage of its parallelism features. We have evaluated the model’s efficiency with different platforms to show the differentiation in terms of time execution and power computation. Based on our knowledge, the results show that our proposed model has achieved the best accuracy (87.15%) and has high efficiency for real-time diagnosis.


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Correspondence to Mohammed A. Fadhel .

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Fadhel, M.A., Al-Shamma, O., Alzubaidi, L., Oleiwi, S.R. (2020). Real-Time Sickle Cell Anemia Diagnosis Based Hardware Accelerator. In: Al-Bakry, A., et al. New Trends in Information and Communications Technology Applications. NTICT 2020. Communications in Computer and Information Science, vol 1183. Springer, Cham.

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  • Print ISBN: 978-3-030-55339-5

  • Online ISBN: 978-3-030-55340-1

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