Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2727
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dc.contributor.authorMishra, Sonali-
dc.contributor.authorMajhi, Banshidhar-
dc.contributor.authorSa, Pankaj Kumar-
dc.contributor.authorSiddiqui, Faiza-
dc.date.accessioned2017-07-06T13:50:32Z-
dc.date.available2017-07-06T13:50:32Z-
dc.date.issued2017-06-
dc.identifier.citation5th International Conference on Advanced Computing, Networking, and Informatics(ICACNI), NIT Goa, India,1-3 June 2017en_US
dc.identifier.urihttp://hdl.handle.net/2080/2727-
dc.descriptionCopyright for this paper belongs to proceeding publisheren_US
dc.description.abstractThis paper proposes a gray level run length matrix (GLRLM) based feature extraction technique for the detection of Acute Lymphoblas-tic Leukemia (ALL). ALL could be a fatal hematopoietic ailment which might cause death if it's not treated at the early stage. The GLRL matrix is a method for extraction of statistical textural features from the nucleus of the lymphocyte image. The extracted features are then supplied to the Support Vector Machine (SVM) for classi_cation. The experiments are performed on an publicly available dataset ALL-IDB1. The accuracy of the proposed scheme is found to be 96.97% for SVM classifieren_US
dc.publisherSpringeren_US
dc.subjectAcute Lymphoblastic Leukemiaen_US
dc.subjectGrey level run lengthen_US
dc.subjectMarker-based watershed segmentationen_US
dc.subjectCAD systemen_US
dc.titleGLRLM based Feature Extraction for Acute Lymphoblastic Leukemia(ALL) Detectionen_US
dc.typeArticleen_US
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