Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3542
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dc.contributor.authorDas, Pradeep Kumar-
dc.contributor.authorJadoun, Priyanka-
dc.contributor.authorMeher, Sukadev-
dc.date.accessioned2020-09-23T06:44:09Z-
dc.date.available2020-09-23T06:44:09Z-
dc.date.issued2020-09-
dc.identifier.citationIEEE HYDCON 2020- International Conference on Engineering in 4th Industrial Revolution (EI4.0), 11-12 September, 2020, Hyderabad, Indiaen_US
dc.identifier.urihttp://hdl.handle.net/2080/3542-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractThe 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.en_US
dc.subjectLeukemiaen_US
dc.subjectClassificationen_US
dc.subjectK-means clusteringen_US
dc.subjectSupport VectorMachineen_US
dc.subjectContrast Limited Adaptive Histogramen_US
dc.subjectEqualizationen_US
dc.titleDetection and Classification of Acute Lymphocytic Leukemiaen_US
dc.typeWorking Paperen_US
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