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

ABSTRACT:

Background

Oral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer. Very few researches have been carried out for the automatic diagnosis of OSCC using artificial intelligence techniques. Though biopsy is the ultimate test for cancer diagnosis, analyzing a biopsy report is a very much challenging task. To develop computer-assisted software that will diagnose cancerous cells automatically is very important and also a major need of the hour.

Aim

To identify OSCC based on morphological and textural features of hand-cropped cell nuclei by traditional machine learning methods.

Methods

In this study, a structure for semi-automated detection and classification of oral cancer from microscopic biopsy images of OSCC, using clinically significant and biolog

SUBMITTER: Rahman TY 

PROVIDER: S-EPMC7941561 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature