Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm
Abstract Oral cancer can occur in different parts of the mouth, including the lips, palate, gums, and inside the cheeks. If not treated in time, it can be life-threatening. Incidentally, using CAD-based diagnosis systems can be so helpful for early detection of this disease and curing it. In this st...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-49438-x |
| _version_ | 1827581626554515456 |
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| author | Wenjing Wang Yi Liu Jianan Wu |
| author_facet | Wenjing Wang Yi Liu Jianan Wu |
| author_sort | Wenjing Wang |
| collection | DOAJ |
| description | Abstract Oral cancer can occur in different parts of the mouth, including the lips, palate, gums, and inside the cheeks. If not treated in time, it can be life-threatening. Incidentally, using CAD-based diagnosis systems can be so helpful for early detection of this disease and curing it. In this study, a new deep learning-based methodology has been proposed for optimal oral cancer diagnosis from the images. In this method, after some preprocessing steps, a new deep belief network (DBN) has been proposed as the main part of the diagnosis system. The main contribution of the proposed DBN is its combination with a developed version of a metaheuristic technique, known as the Combined Group Teaching Optimization algorithm to provide an efficient system of diagnosis. The presented method is then implemented in the “Oral Cancer (Lips and Tongue) images dataset” and a comparison is done between the results and other methods, including ANN, Bayesian, CNN, GSO-NN, and End-to-End NN to show the efficacy of the techniques. The results showed that the DBN-CGTO method achieved a precision rate of 97.71%, sensitivity rate of 92.37%, the Matthews Correlation Coefficient of 94.65%, and 94.65% F1 score, which signifies its ability as the highest efficiency among the others to accurately classify positive samples while remaining the independent correct classification of negative samples. |
| first_indexed | 2024-03-08T22:39:06Z |
| format | Article |
| id | doaj.art-1f0c98ea6f654192b4962e8005cc0138 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T22:39:06Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1f0c98ea6f654192b4962e8005cc01382023-12-17T12:16:01ZengNature PortfolioScientific Reports2045-23222023-12-0113111910.1038/s41598-023-49438-xEarly diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithmWenjing Wang0Yi Liu1Jianan Wu2Department of Stomatology, The First Affiliated Hospital of Yangtze UniversityDepartment of Stomatology, The First Affiliated Hospital of Yangtze UniversityExperimental and Practical Teaching Center, Hubei College of Chinese MedicineAbstract Oral cancer can occur in different parts of the mouth, including the lips, palate, gums, and inside the cheeks. If not treated in time, it can be life-threatening. Incidentally, using CAD-based diagnosis systems can be so helpful for early detection of this disease and curing it. In this study, a new deep learning-based methodology has been proposed for optimal oral cancer diagnosis from the images. In this method, after some preprocessing steps, a new deep belief network (DBN) has been proposed as the main part of the diagnosis system. The main contribution of the proposed DBN is its combination with a developed version of a metaheuristic technique, known as the Combined Group Teaching Optimization algorithm to provide an efficient system of diagnosis. The presented method is then implemented in the “Oral Cancer (Lips and Tongue) images dataset” and a comparison is done between the results and other methods, including ANN, Bayesian, CNN, GSO-NN, and End-to-End NN to show the efficacy of the techniques. The results showed that the DBN-CGTO method achieved a precision rate of 97.71%, sensitivity rate of 92.37%, the Matthews Correlation Coefficient of 94.65%, and 94.65% F1 score, which signifies its ability as the highest efficiency among the others to accurately classify positive samples while remaining the independent correct classification of negative samples.https://doi.org/10.1038/s41598-023-49438-x |
| spellingShingle | Wenjing Wang Yi Liu Jianan Wu Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm Scientific Reports |
| title | Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm |
| title_full | Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm |
| title_fullStr | Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm |
| title_full_unstemmed | Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm |
| title_short | Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm |
| title_sort | early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm |
| url | https://doi.org/10.1038/s41598-023-49438-x |
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