Diagnosis of oral cancer using deep learning algorithms
The aim of this study was to use deep learning for the automatic diagnosis of oral cancer, employing images of the lips, mucosa, and oral cavity. A deep convolutional neural network (CNN) model, augmented with data, was proposed to enhance oral cancer diagnosis. We developed a Mobile Net deep CNN d...
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| Format: | Article |
| Language: | English |
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Universidad Politécnica Salesiana
2024-10-01
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| Series: | Ingenius: Revista de Ciencia y Tecnología |
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| Online Access: | https://revistas.ups.edu.ec/index.php/ingenius/article/view/7318 |
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| _version_ | 1825201955442524160 |
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| author | Mayra Alejandra Dávila Olivos Henry Miguel Herrera Del Águila Félix Melchor Santos López |
| author_facet | Mayra Alejandra Dávila Olivos Henry Miguel Herrera Del Águila Félix Melchor Santos López |
| author_sort | Mayra Alejandra Dávila Olivos |
| collection | DOAJ |
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The aim of this study was to use deep learning for the automatic diagnosis of oral cancer, employing images of the lips, mucosa, and oral cavity. A deep convolutional neural network (CNN) model, augmented with data, was proposed to enhance oral cancer diagnosis. We developed a Mobile Net deep CNN designed to detect and classify oral cancer in the lip, mucosa, and oral cavity areas. The dataset comprised 131 images, including 87 positive and 44 negative cases. Additionally, we expanded the dataset by varying cropping, focus, rotation, brightness, and flipping. The diagnostic performance of the proposed CNN was evaluated by calculating accuracy, precision, recall, F1 score, and area under the curve (AUC) for oral cancer. The CNN achieved an overall diagnostic accuracy of 90.9% and an AUC of 0.91 with the dataset for oral cancer. Despite the limited number of images of lips, mucosa, and oral cavity, the CNN method developed for the automatic diagnosis of oral cancer demonstrated high accuracy, precision, recall, F1 score, and AUC when augmented with data.
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| format | Article |
| id | doaj-art-f3ffd21d9d1842f2b7418930b1c9ccbb |
| institution | Kabale University |
| issn | 1390-650X 1390-860X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Universidad Politécnica Salesiana |
| record_format | Article |
| series | Ingenius: Revista de Ciencia y Tecnología |
| spelling | doaj-art-f3ffd21d9d1842f2b7418930b1c9ccbb2025-02-07T16:30:16ZengUniversidad Politécnica SalesianaIngenius: Revista de Ciencia y Tecnología1390-650X1390-860X2024-10-013210.17163/ings.n32.2024.06Diagnosis of oral cancer using deep learning algorithmsMayra Alejandra Dávila Olivos0https://orcid.org/0000-0003-4861-7037Henry Miguel Herrera Del Águila1https://orcid.org/0000-0002-5553-3897Félix Melchor Santos López2https://orcid.org/0000-0001-8598-2151Universidad Nacional Mayor de San MarcosUniversidad Nacional Mayor de San MarcosUniversidad Nacional Mayor de San Marcos The aim of this study was to use deep learning for the automatic diagnosis of oral cancer, employing images of the lips, mucosa, and oral cavity. A deep convolutional neural network (CNN) model, augmented with data, was proposed to enhance oral cancer diagnosis. We developed a Mobile Net deep CNN designed to detect and classify oral cancer in the lip, mucosa, and oral cavity areas. The dataset comprised 131 images, including 87 positive and 44 negative cases. Additionally, we expanded the dataset by varying cropping, focus, rotation, brightness, and flipping. The diagnostic performance of the proposed CNN was evaluated by calculating accuracy, precision, recall, F1 score, and area under the curve (AUC) for oral cancer. The CNN achieved an overall diagnostic accuracy of 90.9% and an AUC of 0.91 with the dataset for oral cancer. Despite the limited number of images of lips, mucosa, and oral cavity, the CNN method developed for the automatic diagnosis of oral cancer demonstrated high accuracy, precision, recall, F1 score, and AUC when augmented with data. https://revistas.ups.edu.ec/index.php/ingenius/article/view/7318Automatic diagnosisconvolutional neural networkdata augmentationdental healthoral canceroral disease |
| spellingShingle | Mayra Alejandra Dávila Olivos Henry Miguel Herrera Del Águila Félix Melchor Santos López Diagnosis of oral cancer using deep learning algorithms Ingenius: Revista de Ciencia y Tecnología Automatic diagnosis convolutional neural network data augmentation dental health oral cancer oral disease |
| title | Diagnosis of oral cancer using deep learning algorithms |
| title_full | Diagnosis of oral cancer using deep learning algorithms |
| title_fullStr | Diagnosis of oral cancer using deep learning algorithms |
| title_full_unstemmed | Diagnosis of oral cancer using deep learning algorithms |
| title_short | Diagnosis of oral cancer using deep learning algorithms |
| title_sort | diagnosis of oral cancer using deep learning algorithms |
| topic | Automatic diagnosis convolutional neural network data augmentation dental health oral cancer oral disease |
| url | https://revistas.ups.edu.ec/index.php/ingenius/article/view/7318 |
| work_keys_str_mv | AT mayraalejandradavilaolivos diagnosisoforalcancerusingdeeplearningalgorithms AT henrymiguelherreradelaguila diagnosisoforalcancerusingdeeplearningalgorithms AT felixmelchorsantoslopez diagnosisoforalcancerusingdeeplearningalgorithms |