Textural pattern classification for oral squamous cell carcinoma

Rahman, Tabassum Yesmin; Mahanta, Lipi B.; Chakraborty, Chandan; Das, Anup; Sarma, Jagannath D.

doi:10.1111/jmi.12611

Cited by 56 publications

(44 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From Table 5, it is evident that in our previous two studies, 1,55 we have achieved higher accuracy rates than this study. It is due to the number of images, that is, cells, that we have considered.…”

Section: Discussionmentioning

confidence: 40%

“…Numerous studies have been carried out for the measures of texture analysis to characterize lesions in the various areas of the body and to differentiate them from the normal tissues 1,5‐7 . 100% classification accuracy was obtained in OSCC detection using textural features 1 . In another study, Rahman et al 55 carried out research on shape, texture and color features of OSCC on whole image strips and achieved good results.…”

Section: Discussionmentioning

confidence: 99%

“…In both the previous two studies, the number of cells (dataset) is more than this study. In the first case where we have analyzed only texture features, we had considered tissue level images, hence the number of training cells are quite larger than this study 1 . Thus in this study, we have also tried to establish a relationship between the number of trained cells and the subsequent result.…”

Section: Discussionmentioning

confidence: 99%

“…Oral cancer is very commonly occurring cancer worldwide, especially in India. Besides various types of oral cancer, squamous cell carcinoma (SCC) is the commonest one 1 . There are three grades of oral squamous cell carcinoma (OSCC): well‐differentiated SCC, moderately differentiated SCC, and poorly differentiated SCC.…”

Section: Introductionmentioning

confidence: 99%

“…While studies on the use of artificial intelligence (AI) in the development of efficient algorithms have been profound in almost all areas of cancer, we present here a comprehensive summary of studies directed on oral cancer. It is observed that there have been few studies concentrating on OSCC of the buccal cavity, where texture analysis has been widely used 1,4‐7 . Al‐Kadi 8 obtained meningioma texture features by four different texture measures.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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 biologically interpretable morphological and textural features, are examined and proposed. Forty biopsy slides were used for the study from which a total of 452 hand‐cropped cell nuclei has been considered for morphological and textural feature extraction and further analysis. After making a comparative analysis of commonly used methods in the segmentation technique, a combined technique is proposed. Our proposed methodology achieves the best segmentation of the nuclei. Henceforth the features extracted were fed into five classifiers, support vector machine, logistic regression, linear discriminant, k‐nearest neighbors and decision tree classifier. Classifiers were also analyzed by training time. Another contribution of the study is a large indigenous cell level dataset of OSCC biopsy images. Results We achieved 99.78% accuracy applying decision tree classifier in classifying OSCC using morphological and textural features. Conclusion It is found that both morphological and textural features play a very important role in OSCC diagnosis. It is hoped that this type of framework will help the clinicians/pathologists in OSCC diagnosis.

show abstract

Section: Discussionmentioning

confidence: 40%