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http://hdl.handle.net/2080/2727
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DC Field | Value | Language |
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dc.contributor.author | Mishra, Sonali | - |
dc.contributor.author | Majhi, Banshidhar | - |
dc.contributor.author | Sa, Pankaj Kumar | - |
dc.contributor.author | Siddiqui, Faiza | - |
dc.date.accessioned | 2017-07-06T13:50:32Z | - |
dc.date.available | 2017-07-06T13:50:32Z | - |
dc.date.issued | 2017-06 | - |
dc.identifier.citation | 5th International Conference on Advanced Computing, Networking, and Informatics(ICACNI), NIT Goa, India,1-3 June 2017 | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/2727 | - |
dc.description | Copyright for this paper belongs to proceeding publisher | en_US |
dc.description.abstract | This 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 classifier | en_US |
dc.publisher | Springer | en_US |
dc.subject | Acute Lymphoblastic Leukemia | en_US |
dc.subject | Grey level run length | en_US |
dc.subject | Marker-based watershed segmentation | en_US |
dc.subject | CAD system | en_US |
dc.title | GLRLM based Feature Extraction for Acute Lymphoblastic Leukemia(ALL) Detection | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2017_ICACNI_SMishra_GLRLM.pdf | 554 kB | Adobe PDF | View/Open |
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