Study on Contribution of Biological Interpretable and Computer-Aided Features Towards the Classification of Childhood Medulloblastoma Cells.
Diagnosis and Prognosis of brain tumour in children is always a critical case. Medulloblastoma is that subtype of brain tumour which occurs most frequently amongst children. Post-operation, the classification of its subtype is most vital for further clinical management. In this paper a novel approac...
| Published in: | Journal of Medical Systems Vol. 42; no. 8; pp. 1 - 2 |
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| Main Authors: | , , , , |
| Format: | equations & formulas pictorial Journal Article |
| Published: |
Springer Nature
Aug2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131094283&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131094283 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2018 vid: 42 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131094283 131094283 131094283 10.1007/s10916-018-1008-4 131094283 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Study on Contribution of Biological Interpretable and Computer-Aided Features Towards the Classification of Childhood Medulloblastoma Cells. aug: au: Das, Daisy Mahanta, Lipi B. Ahmed, Shabnam Baishya, Basanta Kr. Haque, Inamul affil: Center for Computation and Numerical Studies, Institute of Advanced Study in Science and Technology, 781035, Guwahati, India sug: subj: Brain Neoplasms Classification Tumor Cells, Cultured Brain Neoplasms Diagnosis Prognosis Child Critical Care Disease Management Technology World Health Organization Brain Neoplasms Physiopathology Child: 6-12 years ab: Diagnosis and Prognosis of brain tumour in children is always a critical case. Medulloblastoma is that subtype of brain tumour which occurs most frequently amongst children. Post-operation, the classification of its subtype is most vital for further clinical management. In this paper a novel approach of pathological subtype classification using biological interpretable and computer-aided textural features is forwarded. The classifier for accurate features prediction is built purely on the feature set obtained by segmentation of the ground truth cells from the original histological tissue images, marked by an experienced pathologist. The work is divided into five stages: marking of ground truth, segmentation of ground truth images, feature extraction, feature reduction and finally classification. Kmeans colour segmentation is used to segment out the ground truth cells from histological images. For feature extraction we used morphological, colour and textural features of the cells followed by feature reduction using Principal Component Analysis. Finally both binary and multiclass classification is done using Support Vector Method (SVM). The classification was compared using six different classifiers and performance was evaluated employing five-fold cross-validation technique. The accuracy achieved for binary and multiclass classification before applying PCA were 95.4 and 62.1% and after applying PCA were 100 and 84.9% respectively. The run-time analysis are also shown. Results reveal that this technique of cell level classification can be successfully adopted as architectural view can be confusing. Moreover it conforms substantially to the pathologist’s point of view regarding morphological and colour features, with the addition of computer assisted texture feature. pubtype: Academic Journal doctype: equations & formulas pictorial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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