Capsule network based analysis of histopathological images of oral squamous cell carcinoma
Oral cancer is one of the most prevalent malignancy affecting oral cavity. Determining the correct type of oral cancer at the early stages is important in designing a detailed treatment plan and predicting the response of the patient to the treatment being adopted. A major challenge lies in the dete...
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| Format: | Article |
| Language: | English |
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Springer
2022-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820305280 |
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| author | Santisudha Panigrahi Jayshankar Das Tripti Swarnkar |
| author_facet | Santisudha Panigrahi Jayshankar Das Tripti Swarnkar |
| author_sort | Santisudha Panigrahi |
| collection | DOAJ |
| description | Oral cancer is one of the most prevalent malignancy affecting oral cavity. Determining the correct type of oral cancer at the early stages is important in designing a detailed treatment plan and predicting the response of the patient to the treatment being adopted. A major challenge lies in the detection of oral cancer from histopathological images. In oral malignancy diagnosis, the main visual features are generally extracted from the architectural differences of epithelial layers and the appearance of keratin pearls. This paper proposes a new approach for classifying oral cancer using a deep learning technique known as capsule network. Dynamic routing and routing by agreement of capsule network makes it more robust for rotation and affine transformation of augmented oral dataset. This network’s capability of handling pose, view and orientation makes it suitable for analysis of oral cancer histopathological images at an early stage. The performance of cross-validation indicate that the proposed methodology can efficiently classify the histopathological images of Oral Squamous Cell Carcinoma (OSCC) with 97.78% sensitivity, 96.92% specificity and 97.35% accuracy. |
| format | Article |
| id | doaj-art-3bf7377a054f40b685bf7db9133a28d0 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-3bf7377a054f40b685bf7db9133a28d02025-08-20T03:48:36ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-07-013474546455310.1016/j.jksuci.2020.11.003Capsule network based analysis of histopathological images of oral squamous cell carcinomaSantisudha Panigrahi0Jayshankar Das1Tripti Swarnkar2Department of Computer Science and Engineering, SOA Deemed to be University Bhubaneswar, 751030 Odisha, India; Corresponding author.Centre for Genomics and Biomedical Informatics, Institute of Medical Sciences and SUM Hospital, SOA Deemed to be University Bhubaneswar, 751030 Odisha, IndiaDepartment of Computer Application, SOA Deemed to be University Bhubaneswar, 751030 Odisha, IndiaOral cancer is one of the most prevalent malignancy affecting oral cavity. Determining the correct type of oral cancer at the early stages is important in designing a detailed treatment plan and predicting the response of the patient to the treatment being adopted. A major challenge lies in the detection of oral cancer from histopathological images. In oral malignancy diagnosis, the main visual features are generally extracted from the architectural differences of epithelial layers and the appearance of keratin pearls. This paper proposes a new approach for classifying oral cancer using a deep learning technique known as capsule network. Dynamic routing and routing by agreement of capsule network makes it more robust for rotation and affine transformation of augmented oral dataset. This network’s capability of handling pose, view and orientation makes it suitable for analysis of oral cancer histopathological images at an early stage. The performance of cross-validation indicate that the proposed methodology can efficiently classify the histopathological images of Oral Squamous Cell Carcinoma (OSCC) with 97.78% sensitivity, 96.92% specificity and 97.35% accuracy.http://www.sciencedirect.com/science/article/pii/S1319157820305280HistopathologyDeep learningConvolutional neural networkCapsule networkOral cancer |
| spellingShingle | Santisudha Panigrahi Jayshankar Das Tripti Swarnkar Capsule network based analysis of histopathological images of oral squamous cell carcinoma Journal of King Saud University: Computer and Information Sciences Histopathology Deep learning Convolutional neural network Capsule network Oral cancer |
| title | Capsule network based analysis of histopathological images of oral squamous cell carcinoma |
| title_full | Capsule network based analysis of histopathological images of oral squamous cell carcinoma |
| title_fullStr | Capsule network based analysis of histopathological images of oral squamous cell carcinoma |
| title_full_unstemmed | Capsule network based analysis of histopathological images of oral squamous cell carcinoma |
| title_short | Capsule network based analysis of histopathological images of oral squamous cell carcinoma |
| title_sort | capsule network based analysis of histopathological images of oral squamous cell carcinoma |
| topic | Histopathology Deep learning Convolutional neural network Capsule network Oral cancer |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820305280 |
| work_keys_str_mv | AT santisudhapanigrahi capsulenetworkbasedanalysisofhistopathologicalimagesoforalsquamouscellcarcinoma AT jayshankardas capsulenetworkbasedanalysisofhistopathologicalimagesoforalsquamouscellcarcinoma AT triptiswarnkar capsulenetworkbasedanalysisofhistopathologicalimagesoforalsquamouscellcarcinoma |