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Role of Machine Learning in Detection and Classification of Leukemia: A Comparative Analysis

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Abstract

Blood cancers are of different types out of which leukemia is considered the most common. It is found in different age groups of patients. The underlying reasons of this abnormal illness include excessive blood cell proliferation and immature blood cell growth, which can affect red blood cells, bone marrow, and the immune system. Leukocytes are prominent factor in early detection to identify the diagnosis of leukemia which is early sign of illness. Despite the high prevalence of leukemia, flow cytometry equipment is scarce, and the processes utilized at laboratory diagnosis facilities are time-consuming. In the health sector AI/ML can significantly improve the diagnostic accuracy, reduction of time, and provide cost effective, much safer, and prompt diagnostic possibilities. As AI/ML tools are user friendly, various other experts like clinical, medical laboratories, and specialists can wisely adopt these tools and applications. A thorough and organized overview of the ML-based leukemia detection and classification models is provided in this chapter. The early picture of leukemia can be processed using various ML/AI algorithm applications. The purpose is to improve accuracy, reduce time, and offer trouble-free diagnostic methods. The present systematic review was undertaken to examine the works intending to identify and categorize leukemia by utilizing machine learning algorithms. This study was motivated by the possibilities of machine learning (ML) in disease diagnosis. This review study presents a comprehensive and systematic view of the ML-based leukemia detection and classification models.

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Author notes

  1. Ruchi Garg, Harsh Garg, Harshita Patel, Gayathri Ananthakrishnan, and Suvarna Sharma have contributed equally to this chapter

Authors and Affiliations

  1. Information Technology, Birla Institute of Management Technology, Greater Noida, Uttar Pradesh, India

    Ruchi Garg

  2. Department of Electrical Engineering, Delhi Technological University, New Delhi, India

    Harsh Garg

  3. School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Harshita Patel & Gayathri Ananthakrishnan

  4. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Suvarna Sharma

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  1. Ruchi Garg
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Correspondence to Harshita Patel .

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  1. Gautam Buddha University, Greater Noida, India

    Arun Solanki

  2. SOIL School of Business Design, Greater Noida, India

    Mohd Naved

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Garg, R., Garg, H., Patel, H., Ananthakrishnan, G., Sharma, S. (2023). Role of Machine Learning in Detection and Classification of Leukemia: A Comparative Analysis. In: Solanki, A., Naved, M. (eds) GANs for Data Augmentation in Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-43205-7_1

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  • DOI: https://doi.org/10.1007/978-3-031-43205-7_1

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