Open AccessAutomated acute lymphoblastic leukaemia detection system using microscopic images
Author(s) -
Sukhia Komal Nain,
Ghafoor Abdul,
Riaz Muhammad Mohsin,
Iltaf Naima
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5471
Subject(s) - pattern recognition (psychology) , artificial intelligence , computer science , feature extraction , segmentation , feature selection , scheme (mathematics) , principal component analysis , feature (linguistics) , representation (politics) , selection (genetic algorithm) , mathematics , mathematical analysis , linguistics , philosophy , politics , political science , law
An automatic and novel approach for acute lymphoblastic leukaemia classification is proposed. The proposed scheme is based on pre‐processing and segmentation of white blood cell nuclei using expectation maximisation algorithm, feature extraction, feature selection using principal component analysis and classification using sparse representation. The accuracy of the proposed scheme significantly outperforms the existing schemes in terms of acute lymphoblastic leukaemia classification.