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Review of Decision Tree-Based Binary Classification Framework Using Robust 3D Image and Feature Selection for Malaria-Infected Erythrocyte Detection

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Data Engineering and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1079))

Abstract

We start with a famous proverb ‘health is wealth.’ Malaria is one of the most rapidly spreading and contagious diseases, mostly spread through microbes. Efficient treatment of the disease requires early and accurate estimation to ensure control from spreading and treatment in early phases. Accordingly, several studies have been put forward during the past decade. Analyzing the blood smear’s images is one of the prominent works proposed in this context. This manuscript attempts to automate the process of diagnosis through machine learning techniques. The algorithm trains the model through different selected features of the input images and thereby uses the learning experience to classify the blood smears as disease prone or healthy. The cuckoo search algorithm is used for designing a heuristic scale, which is further assessed through multiple experiments to evaluate its accuracy. Different performance evaluation measures like precision, sensitivity, specificity, and accuracy are used to assess the robustness of the model toward early identification of malaria in the premature stage.

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Author information

Authors and Affiliations

  1. Asst. Prof., Department of information Technology, Muffakham Jah College of Engineering & Technology, Hyderabad, India

    Syed Azar Ali

  2. Prof. & Head, Department of Computer Science & Engg., GITAM University, Hyderabad, India

    S. Phani Kumar

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  1. Syed Azar Ali
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  2. S. Phani Kumar
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Correspondence to Syed Azar Ali .

Editor information

Editors and Affiliations

  1. Professor & Head, Department of Computer Science & Engineering, CMR Technical Campus, Hyderabad, India

    K. Srujan Raju

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Roman Senkerik

  3. Stanley College of Engineering and Technology, Hyderabad, Telangana, India

    Satya Prasad Lanka

  4. Department of EEE, Stanley College of Engineering and Technology, Hyderabad, Telangana, India

    V. Rajagopal

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Azar Ali, S., Phani Kumar, S. (2020). Review of Decision Tree-Based Binary Classification Framework Using Robust 3D Image and Feature Selection for Malaria-Infected Erythrocyte Detection. In: Raju, K.S., Senkerik, R., Lanka, S.P., Rajagopal, V. (eds) Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-15-1097-7_64

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