Histopathological imaging database for oral cancer analysis

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

doi:10.1016/j.dib.2020.105114

Cited by 51 publications

(28 citation statements)

References 4 publications

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“…In this system for the classification of the histopathological images, ML approach is utilized. Employing more than 1000 histopathological images (290 normal and 934 oral cancer images) from the database [18], the performance of the system is discussed in detail. In order to remove background noises and hairs, the image is first preprocessed using the intensity of colour channel separation technique.…”

Section: Resultsmentioning

confidence: 99%

Histopathological Image Analysis for Oral Cancer Classification by Support Vector Machine

Bakare¹,

Kumarasamy²

2021

IJASIS

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Oral cancer is caused by the mutation of the cells in the lips or in the mouth. The incidence rate and prevalence rate of oral cancer are increasing worldwide. Recently, the Machine Learning (ML) approaches play a vital role in medical image diagnosis. They provide accurate and rapid evaluation of the analysis of histopathological images using supervised learning. In this study, three different modules are developed namely preprocessing, feature extraction and classification module. Initially, the raw histopathological image is given to the median filter for the removal of background noise in the preprocessing module. In the next module, the temporal features such as energy, entropy etc., are extracted from the color components of the filtered images. Finally, the classification is done by employing the Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) to classify histopathological images as normal or abnormal. Results show that the SVM classifier is better than KNN for the classification of oral cancer. The classification accuracy on 1224 histopathological images has been improved to 98% by using SVM classifier as compared with the KNN results of 83%.

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Section: Resultsmentioning

confidence: 99%

Histopathological Image Analysis for Oral Cancer Classification by Support Vector Machine

Bakare¹,

Kumarasamy²

2021

IJASIS

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“…Vignettes of H&E stained oral biopsy images from the OSCC dataset by Tabassum et al [19] capturing normal epithelium (a) and cancerous epithelium(b)…”

Section: Data and Methodologiesmentioning

confidence: 99%

“…The OSCC dataset is publicly available and was published by Tabassum et al [19]. It is composed of 1224 oral histopathological images (290 non-cancerous and 934 cancerous) from 230 patients.…”

Section: Datasetmentioning

confidence: 99%

Histopathological Image Analysis for Oral Squamous Cell Carcinoma classification using concatenated deep learning models

Amin¹,

Zamir²,

Khan

2021

Preprint

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Oral squamous cell carcinoma (OSCC) is a subset of head and neck squamous cell carcinoma (HNSCC), the 7th most common cancer worldwide, and accounts for more than 90% of oral malignancies. Early detection of OSCC is essential for effective treatment and reducing the mortality rate. However, the gold standard method of microscopy-based histopathological investigation is often challenging, time-consuming and relies on human expertise. Automated analysis of oral biopsy images can aid the histopathologists in performing a rapid and arguably more accurate diagnosis of OSCC. In this study, we present deep learning (DL) based auto- mated classification of 290 normal and 934 cancerous oral histopathological images published by Tabassum et al (Data in Brief, 2020). We utilized transfer learning approach by adapt- ing three pre-trained DL models to OSCC detection. VGG16, InceptionV3, and Resnet50 were fine-tuned individually and then used in concatenation as feature extractors. The con- catenated model outperformed the individual models and achieved 96.66% accuracy (95.16% precision, 98.33% recall, and 95.00% specificity) compared to 89.16% (VGG16), 94.16% (In- ceptionV3) and 90.83% (ResNet50). These results demonstrate that the concatenated model can effectively replace the use of a single DL architecture.

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“…Later, skilled and certified pathologists have selected the region of interest (ROI), that is, cell nucleus, which was used for ground truth preparation. After that, we have hand‐cropped the cells from the original image and created the nuclei dataset 31,32 . The cropped images were of various sizes depending upon the varying size of the cell.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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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.

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Histopathological imaging database for oral cancer analysis

Cited by 51 publications

References 4 publications

Histopathological Image Analysis for Oral Cancer Classification by Support Vector Machine

Histopathological Image Analysis for Oral Cancer Classification by Support Vector Machine

Histopathological Image Analysis for Oral Squamous Cell Carcinoma classification using concatenated deep learning models

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

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