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 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.

The Effectiveness of Artificial Intelligence in Detection of Oral Cancer

“… The in-depth analysis showed that SVM and linear discriminant classifiers gave the best results for texture and colour features. 9 Shahul Hameed et al 26 India No. of patients = 40 -27 slides -118 normal cells -334 malignant slides -Total of 452 extracted morphologic features Histologic images 1.…”

Applications of artificial intelligence in dentistry: A comprehensive review

“…127 Other types of biological or clinical data have been used for the disease detection, such as rRNA, 123 microbial profiles, 125 or other clinical features. 126 Several approaches have been proposed in literature for oral cancer diagnosis [128][129][130][131] and survival prediction [132][133][134][135] using ML algorithms. For survival prediction, an accuracy of 76% was achieved using a decision tree and clinical features for a global, recurrence-free 5-year survival, 134 similar to other results.…”

“…Several approaches have been proposed in literature for oral cancer diagnosis 128–131 and survival prediction 132–135 using ML algorithms. For survival prediction, an accuracy of 76% was achieved using a decision tree and clinical features for a global, recurrence‐free 5‐year survival, 134 similar to other results 133 .…”

“…105 Color matching 120 Radiography, [110][111][112]115 CBCT Images, 105,116 clinical/other types of data, [117][118][119][120] other image formats 113,114 ML techniques Disease identification Dental caries, 13,121,122 periodontal disease, [123][124][125][126][127] oral cancer, [128][129][130][131][132][133][134][135] dental pain, 136 oral malodour, 137 oral clefts detection. 138 Oral disease prevention 139 Radiography, 122 other image formats, 121,128,129 clinical/biological data 13,[123][124][125][126][127][130][131][132][133]…”

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

“…The results of Fig16, 17, 18 shown that there is still an overfitting phenomenon.However, the good news is that trained DenseNet201 shows good performance in these three combinations.The results show that the Relief-SVM model built by machine learning is more suitable for the classification of lung histopathology images with a small data set. It means that for a limited number of data sets, especially small medical data sets, machine learning methods are often more effective for classification problems[91][92][93][94]. In addition, the CNN features extracted from the DenseNet201 structure also prove from the side that it also plays a role in the task of classification of lung cancer subtypes.This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”