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International Conference on Swarm Intelligence

ICSI 2019: Advances in Swarm Intelligence pp 142–151Cite as

Acute Lymphoblastic Leukemia Cell Detection in Microscopic Digital Images Based on Shape and Texture Features

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11656))

Abstract

Leukemia or blood cancer is a disease that affects a large population, especially children. Fast and early detection of four main types of leukemia is crucial for successful treatment and patient’s recovery. Leukemia can be detected in microscope blood images by detecting blasts, i.e. not fully developed white blood cells. Computer-aided diagnostic systems can improve the quality and speed of abnormal lymphocytes detection. In this paper we proposed a method for automatic detection of one type of leukemia, acute lymphoblastic leukemia, by classifying white blood cells into normal cells and blasts. The proposed method uses shape and texture features as input vector for support vector machine optimized by bare bones fireworks algorithm. Based on the results obtained on the standard benchmark set, ALL-IDB, our proposed method shows a competitive accuracy of classification comparing to other state-of-the-art method.

This research is supported by Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.

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Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

    Eva Tuba, Ivana Strumberger, Nebojsa Bacanin, Dejan Zivkovic & Milan Tuba

Authors
  1. Eva Tuba
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  2. Ivana Strumberger
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  3. Nebojsa Bacanin
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  4. Dejan Zivkovic
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  5. Milan Tuba
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Corresponding author

Correspondence to Milan Tuba .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Tuba, E., Strumberger, I., Bacanin, N., Zivkovic, D., Tuba, M. (2019). Acute Lymphoblastic Leukemia Cell Detection in Microscopic Digital Images Based on Shape and Texture Features. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11656. Springer, Cham. https://doi.org/10.1007/978-3-030-26354-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-26354-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26353-9

  • Online ISBN: 978-3-030-26354-6

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