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Classification of Red Blood Cells in Sickle Cell Anemia Using Deep Convolutional Neural Network

  • Laith Alzubaidi
  • Omran Al-Shamma
  • Mohammed A. FadhelEmail author
  • Laith Farhan
  • Jinglan Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

Sickle cell anemia is an abnormal red blood cell which leads to blood vessel obstruction joined by painful episodes and even death. It is also called abnormal hemoglobin. Hemoglobin is responsible for passing oxygen through the blood vessel for all over the body. Normal red blood cells are in a circular shape and they are compact and flexible, enabling them to move freely through small capillaries. On the other hand, abnormal red blood cells are in sickle shape and they are stiff and angular causing them to become stuck in small capillaries. Due to that, it will be a reason for pain to patients and lead to low oxygen and dehydration. The manual assessment, classification, and counting of biological cells require for an immense spending of time and it may lead to wrong classification and counting since red blood cells are millions in one smear. Also, cells classification is challenging due to heterogeneous and complex shapes, overlapped cells and a variety of colors. We overcome these drawbacks by introducing a new robust and effective deep Convolutional Neural Network to classify Red Blood Cells (RBCs) in three classes namely: normal (‘N’) abnormal (sickle cells anemia type (‘S’)) and miscellaneous (‘M’). In order to improve the results further, we have used our model as features extractor then we applied an error-correcting output codes (ECOC) classifier for the classification task. Our model with ECOC showed outstanding performance and high accuracy of 92.06%.

Keywords

Classification Convolutional Neural Network Cells counting Sickle Cell Anemia (SCA) Red blood cells ECOC classifier 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Laith Alzubaidi
    • 1
    • 2
  • Omran Al-Shamma
    • 2
  • Mohammed A. Fadhel
    • 2
    Email author
  • Laith Farhan
    • 3
    • 4
  • Jinglan Zhang
    • 1
  1. 1.Faculty of Science and EngineeringQueensland University of TechnologyBrisbaneAustralia
  2. 2.University of Information Technology and CommunicationsBaghdadIraq
  3. 3.School of EngineeringManchester Metropolitan UniversityManchesterUK
  4. 4.University of DiyalaBaqubahIraq

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