Automated Detection of Acute Leukemia using K-mean Clustering Algorithm
This work addresses the problem of reducing human intervention in leukemia diagnosis for medical applications, but it is incremental as it applies existing methods to a specific domain.
The paper tackles automated detection of acute leukemia from microscopic blood images using image processing and k-means clustering, achieving an accuracy of 92.8% on a dataset of 60 samples.
Leukemia is a hematologic cancer which develops in blood tissue and triggers rapid production of immature and abnormal shaped white blood cells. Based on statistics it is found that the leukemia is one of the leading causes of death in men and women alike. Microscopic examination of blood sample or bone marrow smear is the most effective technique for diagnosis of leukemia. Pathologists analyze microscopic samples to make diagnostic assessments on the basis of characteristic cell features. Recently, computerized methods for cancer detection have been explored towards minimizing human intervention and providing accurate clinical information. This paper presents an algorithm for automated image based acute leukemia detection systems. The method implemented uses basic enhancement, morphology, filtering and segmenting technique to extract region of interest using k-means clustering algorithm. The proposed algorithm achieved an accuracy of 92.8% and is tested with Nearest Neighbor (KNN) and Naive Bayes Classifier on the data-set of 60 samples.