Classification of Leukemia White Blood Cell Cancer using Image Processing and Machine Learning

Authors

  • Gita Reshmi Author
  • Manjunatha K H Author
  • Kiran P V Author

Keywords:

Machine Learning, Cancer

Abstract

There has been a lot of investment in research into leukaemia since it is a deadly blood disease and
people want to find a way to cure it. Using Image Processing and Machine Learning approaches,
mostly implemented in MATLAB, this study proposes a stable and accurate system for classifying
white blood cell malignancy, leukaemia. With an astounding 97% accuracy rate, the suggested
technique demonstrates an outstanding accomplishment. The accuracy of the classifications is ensured
by using the K-Nearest Neighbours (KNN) algorithm, which takes use of the unique characteristics
retrieved from pictures of leukaemia cells. Acquiring images, preprocessing them, segmenting them,
extracting features, classifying them, and finally, analysing their performance are the main steps in the
system's workflow. Performing a number of preliminary operations, such as noise reduction, contrast
enhancement, and normalisation. A denoised representation, a picture with increased contrast, and
normalised results are all produced. Colour segmentation, beginning segmentation, and area
identification of cancer cell nuclei make up the segmentation phase. Among the outcomes are the
ability to see these areas and detect cancer cell nuclei. Finally, the photo processing and machine
learning pipeline is thoroughly examined by the suggested method for leukaemia white blood cell
cancer classification, which not only exhibits an impressive 97% accuracy but also provides an easily
comprehensible visual analysis. Through the provision of accurate classifications and comprehensive
performance assessments, this system contributes significantly to the early and accurate diagnosis of
leukemia, potentially saving lives and improving patient outcomes.

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Published

2019-01-29

How to Cite

Classification of Leukemia White Blood Cell Cancer using Image Processing and Machine Learning. (2019). INTERNATIONAL JOURNAL OF ADVANCED RESEARCH AND REVIEW (IJARR), 4(1), 29-39. https://www.ijarr.org/index.php/ijarr/article/view/9

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