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Leukemia Detection Using Machine and Deep Learning Through Microscopic Images—A Review

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Prognostic Models in Healthcare: AI and Statistical Approaches

Part of the book series: Studies in Big Data ((SBD,volume 109))

Abstract

Leukemia is a blood malignancy that occurs due to neoplastic proliferation of bone marrow precursor cells resulting in impaired immunity, bleeding tendencies, and ineffective erythropoiesis. Therefore, several manual methods have been developed in the past to detect leukemia, but they have proven to be inaccurate and unreliable. The pathologist may recommend a bone marrow examination to confirm and identify the type of leukemia. This traditional method is time-consuming, and the medical professional’s expertise in diagnosing leukemia may have inaccurate. In this chapter, it has been observed that there are numerous studies for the detection of acute leukemia, but there are only a few studies to detect chronic leukemia. Additionally, microscopic-based methods can be used to analyze microscopic smear images and detect the incidence of leukemia automatically and quickly. It also discusses the benefits, drawbacks, and limitations of a variety of traditional Artificial Intelligence-based approaches for detecting leukemia, such as machine learning and deep learning. Hence, this chapter aims to review the existing literature in the field of medical image processing of blood smear images, with a focus on automated leukemia detection. The analysis of various studies shows that deep learning techniques provide the best results compared to machine learning techniques. Hence, the major drawback in recent studies is that most of the research has been done on locally available datasets. Another weakness in the publicly available datasets is that they have a limited number of images. To solve these issues, research community should focus on large publicly available datasets.

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Acknowledgements

This study was supported by Riphah Artificial Intelligence Research (RAIR) Lab, Riphah International University, Faisalabad, Pakistan.

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Authors and Affiliations

  1. Riphah College of Computing, Riphah International University, Faisalabad Campus, Faisalabad, Pakistan

    Qurat Ul Ain, Shahzad Akbar & Sahar Gull

  2. School of Systems and Technology, University of Management and Technology, Lahore, Pakistan

    Muzammil Hussain

  3. School of Clinical Medicine, Zhengzhou University, Zhengzhou, Henan, PR China

    Noor Ayesha

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  1. Qurat Ul Ain
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  2. Shahzad Akbar
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  3. Sahar Gull
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  4. Muzammil Hussain
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  5. Noor Ayesha
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Corresponding author

Correspondence to Shahzad Akbar .

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Editors and Affiliations

  1. Artificial Intelligence and Data Analytics Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Tanzila Saba

  2. Artificial Intelligence and Data Analytics Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Amjad Rehman

  3. Artificial Intelligence and Data Science, Jio Institute, Ulwe, Navi Mumbai, India

    Sudipta Roy

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Qurat Ul Ain, Akbar, S., Gull, S., Hussain, M., Ayesha, N. (2022). Leukemia Detection Using Machine and Deep Learning Through Microscopic Images—A Review. In: Saba, T., Rehman, A., Roy, S. (eds) Prognostic Models in Healthcare: AI and Statistical Approaches. Studies in Big Data, vol 109. Springer, Singapore. https://doi.org/10.1007/978-981-19-2057-8_10

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