Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3542
Title: Detection and Classification of Acute Lymphocytic Leukemia
Authors: Das, Pradeep Kumar
Jadoun, Priyanka
Meher, Sukadev
Keywords: Leukemia
Classification
K-means clustering
Support VectorMachine
Contrast Limited Adaptive Histogram
Equalization
Issue Date: Sep-2020
Citation: IEEE HYDCON 2020- International Conference on Engineering in 4th Industrial Revolution (EI4.0), 11-12 September, 2020, Hyderabad, India
Abstract: The research work aims to develop an automated detection and classification method for acute lymphocytic leukemia (ALL). Extraction of lymphocytes is accomplished by the color based k-means clustering technique. Then, shape, texture, and color features are extracted from the segmented image. Gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) algorithms are used to extract the features of nucleus. Moreover, Principal component analysis (PCA) is applied for dimensional reduction. Finally, an SVM (support vector machine) with an RBF kernel is employed to classify WBCs. The proposed method yields promising results with 96.00% accuracy and 92.64% sensitivity.
Description: Copyright of this document belongs to proceedings publisher.
URI: http://hdl.handle.net/2080/3542
Appears in Collections:Conference Papers

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