Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks.

ABSTRACT: This work aims to classify normal and carcinogenic cells in the oral cavity using two different approaches with an eye towards achieving high accuracy. The first approach extracts local binary patterns and metrics derived from a histogram from the dataset and is fed to several machine-learning models. The second approach uses a combination of neural networks as a backbone feature extractor and a random forest for classification. The results show that information can be learnt effectively from limited training images using these approaches. Some approaches use deep learning algorithms to generate a bounding box that can locate the suspected lesion. Other approaches use handcrafted textural feature extraction techniques and feed the resultant feature vectors to a classification model. The pr

SUBMITTER: Ananthakrishnan B 

PROVIDER: S-EPMC10001077 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature