Segmentation and Classification of Acute Lymphoblastic Leukemia Cells Tooled with Digital Image Processing and ML Techniques

Medical science has been contributing its active role in fighting vigorously against the life ceasing diseases. The algorithm is proposed by investigating the existing segmentation algorithms in the field of leukemia research for the sake of supporting hematopathologists to recognize Acute Lymphocytic Leukemia (ALL) by analyzing the blood cell images. There are four levels of separating and classifying benign and malignant white blood cells (WBC). They are, preprocessing, segmentation, extraction of features and classification. As a preliminary task of image analysis, segmentation is done with morphological operators and Otsu's thresholding. Then utilization of nucleus features with supervised KNN classifier gains the classification accuracy of 95.96%, 95.92 % of sensitivity and 96% of specificity.

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