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

Rahman, Tabassum Yesmin; Mahanta, Lipi B; Choudhury, Hiten; Das, Anup K; Sarma, Jagannath D

Rahman, Tabassum Yesmin; Mahanta, Lipi B; Choudhury, Hiten; Das, Anup K; Sarma, Jagannath D.

Affiliation

Rahman TY; Department of Computer Science & IT, Cotton University, Guwahati, India.
Mahanta LB; Mathematical and Computational Sciences Division, Institute of Advanced Study in Science and Technology, Guwahati, India.
Choudhury H; Department of Computer Science & IT, Cotton University, Guwahati, India.
Das AK; Pathology, Arya Wellness Centre, Guwahati, India.
Sarma JD; Pathology, Dr B. Borooah Cancer Institute, Guwahati, India.

Cancer Rep (Hoboken) ; 3(6): e1293, 2020 12.

Article in En | MEDLINE | ID: mdl-33026718

Subject(s)

Machine Learning; Mouth Neoplasms/pathology; Squamous Cell Carcinoma of Head and Neck/pathology; Biopsy; Humans; Mouth Neoplasms/classification; Mouth Neoplasms/diagnosis; Principal Component Analysis; Squamous Cell Carcinoma of Head and Neck/classification; Squamous Cell Carcinoma of Head and Neck/diagnosis

Key words

ML; OSCC; cell level; cell nuclei; morphological features; oral cancer; textural features

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Mouth Neoplasms / Machine Learning / Squamous Cell Carcinoma of Head and Neck Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Cancer Rep (Hoboken) Year: 2020 Document type: Article

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