International Journal of Recent Trends in Engineering & Research

online ISSN

BLOOD SMEAR ANALYSIS FOR ACUTE LEUKEMIA DETECTION-MACHINE LEARNING APPROACH

Publication Date : 18/12/2018

DOI : 10.23883/IJRTER.2018.4416.YDZFX


Author(s) :

BIJI G.


Volume/Issue :
Volume 4
,
Issue 12
(12 - 2018)



Abstract :

Whenever the normal functioning of the body or any of its part becomes impaired, diseases occur and may require medical treatment. In general, diseases can be classified on the basis of their cause and cell of origin i.e. infectious, immunological, endocrine, genetic, neoplastic, and traumatic etc. Among all diseases the quest for understanding leukemia, a malignant neoplastic disorder is in the research forefront for several investigators including biologists, clinicians, and chemists. Physicians across the globe are interested in understanding the biology of diseases, and how it can be prevented, or treated. Studies reveal that excessive workload, shortage of trained pathologist, and use of conventional hematological evaluation methods are some of the leading causes behind delayed or wrong diagnosis in India. Such shortcomings can be overcome by the utilization of quantitative microscopic techniques in the precise characterization of blood test samples facilitating early diagnosis of blood cancers. The development of an automated system for cancer diagnosis in the scanned microscopic images involves four main computational steps i.e. preprocessing, segmentation, feature extraction and detection. The final machine learning approach that locates circles with accuracy even considering complicated conditions and noisy images. Keywords— Neoplastic, preprocessing, segmentation, feature extraction, Machine learning, Leukemia


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