Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma
Oral cancer can result from mutations in cells located in the lips or mouth. Diagnosing oral cavity squamous cell carcinoma (OCSCC) is particularly challenging, often occurring at advanced stages. To address this, computer-aided diagnosis methods are increasingly being used. In this work, a deep lea...
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Elsevier
2024-12-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016124004850 |
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| author | Prerna Kulkarni Nidhi Sarwe Abhishek Pingale Yash Sarolkar Rutuja Rajendra Patil Gitanjali Shinde Gagandeep Kaur |
| author_facet | Prerna Kulkarni Nidhi Sarwe Abhishek Pingale Yash Sarolkar Rutuja Rajendra Patil Gitanjali Shinde Gagandeep Kaur |
| author_sort | Prerna Kulkarni |
| collection | DOAJ |
| description | Oral cancer can result from mutations in cells located in the lips or mouth. Diagnosing oral cavity squamous cell carcinoma (OCSCC) is particularly challenging, often occurring at advanced stages. To address this, computer-aided diagnosis methods are increasingly being used. In this work, a deep learning-based approach utilizing models such as VGG16, ResNet50, LeNet-5, MobileNetV2, and Inception V3 is presented. NEOR and OCSCC datasets were used for feature extraction, with virtual slide images divided into tiles and classified as normal or squamous cell cancer. Performance metrics like accuracy, F1-score, AUC, precision, and recall were analyzed to determine the prerequisites for optimal CNN performance. The proposed CNN approaches were effective for classifying OCSCC and oral dysplasia, with the highest accuracy of 95.41 % achieved using MobileNetV2. Key findings: Deep learning models, particularly MobileNetV2, achieved high classification accuracy (95.41 %) for OCSCC.CNN-based methods show promise for early-stage OCSCC and oral dysplasia diagnosis. Performance parameters like precision, recall, and F1-score help optimize CNN model selection for this task. |
| format | Article |
| id | doaj-art-e1820ece0e1f484ebcd41bd426fbcc4f |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-e1820ece0e1f484ebcd41bd426fbcc4f2024-11-13T04:29:47ZengElsevierMethodsX2215-01612024-12-0113103034Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinomaPrerna Kulkarni0Nidhi Sarwe1Abhishek Pingale2Yash Sarolkar3Rutuja Rajendra Patil4Gitanjali Shinde5Gagandeep Kaur6Department of CSE (AIML), Vishwakarma Institute of Information Technology, Kondhwa (Budruk) Pune, Maharashtra 411048, IndiaDepartment of CSE (AIML), Vishwakarma Institute of Information Technology, Kondhwa (Budruk) Pune, Maharashtra 411048, IndiaDepartment of CSE (AIML), Vishwakarma Institute of Information Technology, Kondhwa (Budruk) Pune, Maharashtra 411048, IndiaDepartment of CSE (AIML), Vishwakarma Institute of Information Technology, Kondhwa (Budruk) Pune, Maharashtra 411048, IndiaDepartment of CSE (AIML), Vishwakarma Institute of Information Technology, Kondhwa (Budruk) Pune, Maharashtra 411048, IndiaDepartment of CSE (AIML), Vishwakarma Institute of Information Technology, Kondhwa (Budruk) Pune, Maharashtra 411048, IndiaCSE Department, Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India; Corresponding author.Oral cancer can result from mutations in cells located in the lips or mouth. Diagnosing oral cavity squamous cell carcinoma (OCSCC) is particularly challenging, often occurring at advanced stages. To address this, computer-aided diagnosis methods are increasingly being used. In this work, a deep learning-based approach utilizing models such as VGG16, ResNet50, LeNet-5, MobileNetV2, and Inception V3 is presented. NEOR and OCSCC datasets were used for feature extraction, with virtual slide images divided into tiles and classified as normal or squamous cell cancer. Performance metrics like accuracy, F1-score, AUC, precision, and recall were analyzed to determine the prerequisites for optimal CNN performance. The proposed CNN approaches were effective for classifying OCSCC and oral dysplasia, with the highest accuracy of 95.41 % achieved using MobileNetV2. Key findings: Deep learning models, particularly MobileNetV2, achieved high classification accuracy (95.41 %) for OCSCC.CNN-based methods show promise for early-stage OCSCC and oral dysplasia diagnosis. Performance parameters like precision, recall, and F1-score help optimize CNN model selection for this task.http://www.sciencedirect.com/science/article/pii/S2215016124004850Convolutional Neural Networks |
| spellingShingle | Prerna Kulkarni Nidhi Sarwe Abhishek Pingale Yash Sarolkar Rutuja Rajendra Patil Gitanjali Shinde Gagandeep Kaur Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma MethodsX Convolutional Neural Networks |
| title | Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma |
| title_full | Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma |
| title_fullStr | Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma |
| title_full_unstemmed | Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma |
| title_short | Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma |
| title_sort | exploring the efficacy of various cnn architectures in diagnosing oral cancer from squamous cell carcinoma |
| topic | Convolutional Neural Networks |
| url | http://www.sciencedirect.com/science/article/pii/S2215016124004850 |
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