Name
Title | ||
|---|---|---|
Stratified squamous epithelial biopsy image classifier using machine learning and neighborhood feature selection |
Abstract | ||
|---|---|---|
•The main objective of this work is to design and develop a classifier for oral squamous cell carcinoma (OSCC) originating in stratified squamous epithelial tissue of oral cavity. The classifier is named as Stratified Squamous Epithelium Biopsy Image Classifier (SSE-BIC). This algorithm automatize the detection and or classification process which is still a tedious work being done manually via visual inspection by experts.•A total 676 images have been used to design, train and test the classifier. As many as 305 features have been computed from H&E-stained microscopic images of oral mucosa which include colour features, textural features, gradient features, shape features and tamura features to design the SSE-BIC. We used, unsupervised data mining technique to extract cellular regions from the images as a part of shape feature extraction process.•The accuracy observed as an exhaustive simulation is found to be 95.56% using H&E-stained microscopic colour images which has very complicated structure as compared to cytology images. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.bspc.2019.101671 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
H&E-stained microscopic image,Oral squamous cell carcinoma,SVM,NCFS,Decision tree,RS-LDA | Image classifier,Computer vision,Feature selection,Pattern recognition,Biopsy,Artificial intelligence,Oral cavity,Classifier (linguistics),Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
55 | 1746-8094 | 1 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Archana Nawandhar | 1 | 1 | 0.34 |
| Navin Kumar | 2 | 12 | 4.96 |
| Veena R | 3 | 1 | 0.34 |
| Lakshmi Yamujala | 4 | 1 | 0.34 |