NDLI: Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

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Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

Content Provider	Scilit
Author	Praveena, Segu Singh, Sohan Pal
Copyright Year	2020
Abstract	Leukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.
Related Links	https://www.degruyter.com/downloadpdf/journals/bmte/ahead-of-print/article-10.1515-bmt-2018-0213/article-10.1515-bmt-2018-0213.pdf
Ending Page	773
Page Count	15
Starting Page	759
ISSN	00135585
e-ISSN	1862278X
DOI	10.1515/bmt-2018-0213
Journal	Biomedizinische Technik/Biomedical Engineering
Issue Number	6
Volume Number	65
Language	English
Publisher	Walter de Gruyter GmbH
Publisher Date	2020-11-18
Access Restriction	Open
Subject Keyword	Biomedizinische Technik/biomedical Engineering Microscopic Research Classification Deep Convolutional Neural Network Leukaemia Detection Local Directional Patterns Segmentation Sparse Fuzzy C-means Journal: Biomedizinische Technik/Biomedical Engineering, Vol- 65
Content Type	Text
Resource Type	Article
Subject	Medicine Biomedical Engineering

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