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Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms.

Vasundhara Acharya1, Preetham Kumar2

  • 1Department of Computer Science and Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education(MAHE), Manipal, India. vasundhara.acharya@manipal.edu.

Medical & Biological Engineering & Computing
|June 16, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a computer-aided system for accurately segmenting blood smear images to detect acute lymphoblastic leukemia (ALL). The novel algorithm achieved 98.6% accuracy, aiding in early diagnosis and differentiation of ALL subtypes.

Keywords:
Acute lymphoblastic leukemiaBlood smear cellsFlow cytometryHemocytometerWhite blood cell

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Hematology

Background:

  • Acute lymphoblastic leukemia (ALL) is a significant blood cancer, particularly affecting children and older adults.
  • Current diagnostic methods like flow cytometry and manual cell counts have limitations in accuracy and precision.
  • Early and accurate diagnosis of ALL is crucial for improving patient outcomes and reducing mortality rates.

Purpose of the Study:

  • To survey computer-aided techniques for blood smear image segmentation.
  • To develop a novel algorithm for accurate segmentation of white blood cell nucleus and cytoplasm.
  • To build and train a model for feature extraction and classification of ALL, overcoming limitations of existing methods.

Main Methods:

  • A novel algorithm was developed for segmenting white blood cell nucleus and cytoplasm.
  • Feature extraction and model training were performed using supervised classifiers.
  • InfoGainAttributeEval and Ranker Search were employed for feature selection to enhance classifier performance.
  • The system was trained and evaluated on 600 blood smear images, classifying ALL into L1, L2, and L3 subtypes.

Main Results:

  • The developed model successfully differentiated between normal and abnormal blood smears.
  • The system achieved an overall accuracy of 98.6% in classifying acute lymphoblastic leukemia.
  • Extracted feature values clearly distinguished between cancerous and normal cells.
  • The algorithm demonstrated effectiveness across various staining methods.

Conclusions:

  • The proposed computer-aided system offers a highly accurate method for blood smear image segmentation and ALL classification.
  • The developed algorithm can serve as a valuable diagnostic tool for pathologists, potentially improving early detection rates.
  • This approach addresses the limitations of traditional diagnostic methods, offering a more precise and efficient solution for ALL diagnosis.