Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques
| Autor: | Anup K. Das, Hiten Choudhury, Jagannath D. Sarma, Lipi B. Mahanta, Tabassum Yesmin Rahman |
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| Rok vydání: | 2020 |
| Předmět: |
Cancer Research
Computer science Biopsy Training time Cellular level Machine learning computer.software_genre Machine Learning medicine Humans Basal cell Segmentation Principal Component Analysis medicine.diagnostic_test Squamous Cell Carcinoma of Head and Neck business.industry Decision tree learning Original Articles oral cancer Linear discriminant analysis ML Support vector machine cell level stomatognathic diseases cell nuclei morphological features Oncology textural features Mouth Neoplasms Original Article OSCC Artificial intelligence business computer |
| Zdroj: | Cancer Reports |
| ISSN: | 2573-8348 |
| DOI: | 10.1002/cnr2.1293 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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