About: Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques

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scientific article published on 07 October 2020

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rdf:type	Item
description	wetenschappelijk artikel (nl) artículu científicu espublizáu n'ochobre de 2020 (ast) im Oktober 2020 veröffentlichter wissenschaftlicher Artikel (de) scientific article published on 07 October 2020 (en) наукова стаття, опублікована 7 жовтня 2020 (uk)
publication date	wds:Q100480374-D9FE6881-33AC-4F80-BBB1-F8EEC3E2B927
publication date	2020-10-07 00:00:00Z (xsd:dateTime)
cites work	wds:Q100480374-0070FB63-C6EF-4DC2-B976-3065C217357E wds:Q100480374-00C61C2E-D6CA-44F4-BF0E-0624BAE30B55 wds:Q100480374-102EE156-E52D-4FEC-BC33-FC117767F13A wds:Q100480374-14193167-3D28-41ED-9AF3-A7C65840E112 wds:Q100480374-166C7FB9-462D-4E85-84CF-25AECD40A69B wds:Q100480374-19843B57-CEB1-4D01-9DCA-07F1BEC6E245 wds:Q100480374-23A3DE58-A869-448C-BCC6-2BDFE0F0AEB7 wds:Q100480374-2D7E8B20-CEB0-48F3-8AD0-5F7AA7508E9E wds:Q100480374-2EC01F9C-3E05-421F-A765-821CF2050361 wds:Q100480374-3DD1846A-BBA6-482F-9DF9-2F6B010EEB99 wds:Q100480374-4201D082-B7A2-421A-B85E-7678D140A74A wds:Q100480374-4582A461-B736-45DC-842E-BB1A2F09231E wds:Q100480374-5C6BCEC5-6062-41E8-AB1D-DFC299056D3D wds:Q100480374-77C5563E-343E-41C9-BDC6-A40C08D74B62 wds:Q100480374-821A137D-7E03-4E0C-9BCD-2A8D7EBA7C2E wds:Q100480374-842AF727-D779-4AEC-AACD-C9CF95AF306B wds:Q100480374-8F33452C-9E77-4E20-BDCE-5D9F32AA4602 wds:Q100480374-9BCE6389-0C3F-4AE1-A02D-4BA0085F27A2 wds:Q100480374-A667F30B-B029-4957-BC19-BC8C273199E4 wds:Q100480374-B9CC5256-202D-40C0-AB6E-25C540891B66 wds:Q100480374-C0426188-5EBD-4A01-B8F7-AA7F5A6A102E wds:Q100480374-C7225680-DF40-4423-BF85-273D74CCDF67 wds:Q100480374-E031B05C-E240-42C1-8759-3A60AA10E6C3 wds:Q100480374-E28EB94A-7C33-41F3-8B75-34E5554618EA wds:Q100480374-F301A317-73A4-406B-86C6-676EDFF342F1 wds:Q100480374-F799878B-0329-4286-AD7D-A587353A0C0A wds:Q100480374-F7ECF5F9-054F-452B-81E2-75E25CC86A7F wds:Q100480374-FA25E70E-C384-4A69-87E4-C283577B666C wds:Q100480374-FA571681-3F9A-443A-AD07-9C1DB79728FF
cites work	An introduction to ROC analysis HIF1A signaling selectively supports proliferation of breast cancer in the brain Histograms of Oriented Gradients for Human Detection A Tutorial on Support Vector Machines for Pattern Recognition Textural Features Corresponding to Visual Perception Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation. Medical image analysis: progress over two decades and the challenges ahead Texture information in run-length matrices. A support vector machine approach for detection of microcalcifications. Early prognosis of metastasis risk in inflammatory breast cancer by texture analysis of tumour microscopic images. A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma. Texture analysis of aggressive and nonaggressive lung tumor CE CT images Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm. A comparative study of texture measures with classification based on featured distributions Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features Texture analysis of CT images in the characterization of oral cancers involving buccal mucosa. Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm Statistical analysis of textural features for improved classification of oral histopathological images. Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps Automated identification of keratinization and keratin pearl area from in situ oral histological images. Textural pattern classification for oral squamous cell carcinoma. Histopathological imaging database for oral cancer analysis Feature selection based on artificial bee colony and gradient boosting decision tree Fractal dimension of time-resolved autofluorescence discriminates tumour from healthy tissues in the oral cavity. Computer vision approach to morphometric feature analysis of basal cell nuclei for evaluating malignant potentiality of oral submucous fibrosis Textural characterization of histopathological images for oral sub-mucous fibrosis detection. Automated oral squamous cell carcinoma identification using shape, texture and color features of whole image strips
author name string	wds:Q100480374-1E3E6AD1-D802-49A9-B52D-C8D479DAE3CE wds:Q100480374-3B816CBB-DD03-47CE-AB19-043868D9F0AE wds:Q100480374-AC6FBB8E-F4F0-422B-80D0-5A2910FB0763 wds:Q100480374-E26CCEF1-906E-402F-8C86-263CAE82635B
author name string	Anup K Das Jagannath D Sarma Tabassum Yesmin Rahman Hiten Choudhury
rdfs:label	Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques (dv) Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques (en) Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques (nl)
skos:prefLabel	Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques (dv) Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques (en) Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques (nl)
name	Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques (dv) Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques (en) Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques (nl)
author	wds:Q100480374-66FAEEFF-D8E8-4973-B68D-3415D8E0CCD3
author	Lipi B. Mahanta
title	wds:Q100480374-36E74377-474E-4A88-9762-7FC71FCF97DF
title	Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques (en)
page(s)	wds:Q100480374-48E1A7C7-BD5C-4BCE-B2AA-B1F9E0B7DFA3
page(s)	e1293
instance of	wds:Q100480374-BF4C6FEE-7BC2-4ED2-8941-2E7028A8CDEA
instance of	scholarly article
main subject	wds:Q100480374-518682B6-04EA-47CF-BA22-3F1403C655B6 wds:Q100480374-B84F554F-B0F0-4626-A983-4A3E612814AE
main subject	machine learning oral squamous cell carcinoma
PubMed ID	wds:Q100480374-7A267F1C-823A-4D47-BB3A-1D3508727A20
PubMed ID	http://rdf.ncbi.nlm.nih.gov/pubchem/reference/33026718
PubMed ID	33026718
published in	wds:Q100480374-11C2437E-CBF7-42CF-A497-8915AA1A6455
published in	Cancer Reports

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