Nucleus and cytoplasm-based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images.
Acute lymphoblastic leukaemia (ALL), which is due to the malfunctioning in the bone marrow, is common among people all over the world. The haematologist suffers a lot to discriminate the presence of leukaemia in the patients using the blood smears. To overcome the inaccuracy and reliability issues,...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 1; pp. 171 - 187 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141101362&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141101362 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2020 vid: 58 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141101362 141101362 NLM31811554 10.1007/s11517-019-02071-1 NLM31811554 141101362 ppf: 171 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Nucleus and cytoplasm-based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images. aug: au: Jha, Krishna Kumar Dutta, Himadri Sekhar affil: Calcutta Institute of Technology, Banitabla, Uluberia, Howrah – 711316, India sug: subj: Leukemia, Lymphocytic, Acute Diagnosis Image Processing, Computer Assisted Cell Nucleus Pathology Leukemia, Lymphocytic, Acute Blood Logic Reproducibility of Results Physics Algorithms Ferrans and Powers Quality of Life Index ab: Acute lymphoblastic leukaemia (ALL), which is due to the malfunctioning in the bone marrow, is common among people all over the world. The haematologist suffers a lot to discriminate the presence of leukaemia in the patients using the blood smears. To overcome the inaccuracy and reliability issues, this paper proposes an automatic method of leukaemia detection, named chronological Sine Cosine Algorithm-based actor-critic neural network (Chrono-SCA-ACNN). Initially, the blood smear images are segmented using the proposed entropy-based hybrid model, from which the image-level features and statistical features are extracted from the segments. Then, the selected features are applied to the proposed classifier, which detects the leukaemia. In the proposed Chrono-SCA-ACNN, the optimal weights are selected by the proposed Chrono-SCA, which is the integration of the chronological concept in the SCA. Finally, the experimentation is performed using the ALL-IDB2 database, and the effectiveness of the proposed method over the existing methods is evaluated. From the analysis, the accuracy of the proposed method is found to be 0.99, which proves that it outperforms the existing classification methodologies. Graphical abstract Block diagram of proposed Leukaemia detection. The main aim of the paper is to segment and classify the WBCs for ALL detection in single cell blood smear images. Initially, the blood smear image is subjected to pre-processing in order to enhance the quality of the input image so as to make it effective for the further processes associated with Leukaemia detection. The pre-processed image is applied to the segmentation process that segments the cytoplasm and nucleus using the Entropy-based hybrid model. The entropy-based hybrid model is developed using the FCM and active contour to segment the cytoplasm and nucleus that is fused using the entropy. The segments are subjected to the feature extraction that extracts the statistical features and the color histogram-based features from the segments. The features are presented to the Actor-Critic Neural Network and the weights of the Neural Network (NN) are optimally tuned using the proposed Chrono-SCA. The block diagram of the proposed method of leukaemia detection is depicted in Fig. 1. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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