GLRLM-Based Feature Extraction for Acute Lymphoblastic Leukemia (ALL) Detection

被引：18

作者：

Mishra, Sonali ^{[1
]}

Majhi, Banshidhar ^{[1
]}

Sa, Pankaj Kumar ^{[1
]}

机构：

[1] Natl Inst Technol, Dept Comp Sci & Engn, Pattern Recognit Res Lab, Rourkela 769008, India

来源：

RECENT FINDINGS IN INTELLIGENT COMPUTING TECHNIQUES, VOL 2 | 2018年 / 708卷

关键词：

Acute Lymphoblastic Leukemia; Gray-level run length; Marker-based watershed segmentation; CAD system;

D O I：

10.1007/978-981-10-8636-6_41

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work presents gray-level run length (GLRL) matrix as feature extraction technique for the classification of Acute Lymphoblastic Leukemia (ALL). ALL detection in an early stage is helpful in avoiding fatal hematopoietic ailment which might cause death. The GLRL matrix extracts textural features from the nucleus of the lymphocyte image which is used with Support Vector Machine (SVM) for classification. The public dataset ALL-IDB1 is used for the experiment, and we have obtained 96.97% accuracy for GLRL feature with SVM classifier.

引用

页码：399 / 407

页数：9

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