PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image
Colon polyps are small, precancerous growths in the colon that can indicate colorectal cancer (CRC), a disease that has a significant impact on public health. A colonoscopy is a medical procedure that helps detect colon polyps. However, the manual examination for identifying the type of polyps can b...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Informatics in Medicine Unlocked |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914825000425 |
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| author | Khaled Eabne Delowar Mohammed Borhan Uddin Md Khaliluzzaman Riadul Islam Rabbi Md Jakir Hossen M. Moazzam Hossen |
| author_facet | Khaled Eabne Delowar Mohammed Borhan Uddin Md Khaliluzzaman Riadul Islam Rabbi Md Jakir Hossen M. Moazzam Hossen |
| author_sort | Khaled Eabne Delowar |
| collection | DOAJ |
| description | Colon polyps are small, precancerous growths in the colon that can indicate colorectal cancer (CRC), a disease that has a significant impact on public health. A colonoscopy is a medical procedure that helps detect colon polyps. However, the manual examination for identifying the type of polyps can be time-consuming, tedious, and prone to human error. Automatic classification of polyps through colonoscopy images can be more efficient. However, there are currently no specialized methods for the classification of polyps from colonoscopy; however, several state-of-the-art CNN models can classify polyps. We are introducing a new CNN-based model called PolyNet, a model that shows the best accuracy of the polyps classification from the multiple models and which also performs better than pre-trained models such as VGG16, ResNet50, DenseNetV3, MobileNetV3, and InceptionV3, as well as nine other customized CNN-based models for classification. This study provides a sensitivity analysis to demonstrate how slight modifications in the network's architecture can impact the balance between accuracy and performance. We examined different CNN architectures and developed a good convolutional neural network (CNN) model for correctly predicting colon polyps using the Kvasir dataset. The self-attention mechanism is incorporated in the best CNN model, i.e., PolypNet, to ensure better accuracy. To compare, DenseNetV3, MobileNet-V3, Inception-V3, VGG16, and ResNet50 get 73.87 %, 69.38 %, 61.12 %, 84.00 %, and 86.12 % of accuracy on the Kvasir dataset, while PolypNet with attention archives 86 % accuracy, 86 % precision, 85 % recall, and an 86 % F1-score. |
| format | Article |
| id | doaj-art-841f4361607b4274b0e737b2cec2bdfe |
| institution | OA Journals |
| issn | 2352-9148 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj-art-841f4361607b4274b0e737b2cec2bdfe2025-08-20T02:33:55ZengElsevierInformatics in Medicine Unlocked2352-91482025-01-015610165410.1016/j.imu.2025.101654PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy imageKhaled Eabne Delowar0Mohammed Borhan Uddin1Md Khaliluzzaman2Riadul Islam Rabbi3Md Jakir Hossen4M. Moazzam Hossen5Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh; International Islamic University Chittagong (IIUC), Chittagong, BangladeshChittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh; International Islamic University Chittagong (IIUC), Chittagong, BangladeshInternational Islamic University Chittagong (IIUC), Chittagong, BangladeshFaculty of Engineering and Technology, Multimedia University, Melaka, MalaysiaFaculty of Engineering and Technology, Multimedia University, Melaka, Malaysia; Corresponding author.International Islamic University Chittagong (IIUC), Chittagong, BangladeshColon polyps are small, precancerous growths in the colon that can indicate colorectal cancer (CRC), a disease that has a significant impact on public health. A colonoscopy is a medical procedure that helps detect colon polyps. However, the manual examination for identifying the type of polyps can be time-consuming, tedious, and prone to human error. Automatic classification of polyps through colonoscopy images can be more efficient. However, there are currently no specialized methods for the classification of polyps from colonoscopy; however, several state-of-the-art CNN models can classify polyps. We are introducing a new CNN-based model called PolyNet, a model that shows the best accuracy of the polyps classification from the multiple models and which also performs better than pre-trained models such as VGG16, ResNet50, DenseNetV3, MobileNetV3, and InceptionV3, as well as nine other customized CNN-based models for classification. This study provides a sensitivity analysis to demonstrate how slight modifications in the network's architecture can impact the balance between accuracy and performance. We examined different CNN architectures and developed a good convolutional neural network (CNN) model for correctly predicting colon polyps using the Kvasir dataset. The self-attention mechanism is incorporated in the best CNN model, i.e., PolypNet, to ensure better accuracy. To compare, DenseNetV3, MobileNet-V3, Inception-V3, VGG16, and ResNet50 get 73.87 %, 69.38 %, 61.12 %, 84.00 %, and 86.12 % of accuracy on the Kvasir dataset, while PolypNet with attention archives 86 % accuracy, 86 % precision, 85 % recall, and an 86 % F1-score.http://www.sciencedirect.com/science/article/pii/S2352914825000425Colon polypConvolutional neural network (CNN)Self-attentionTransfer learningColonoscopy |
| spellingShingle | Khaled Eabne Delowar Mohammed Borhan Uddin Md Khaliluzzaman Riadul Islam Rabbi Md Jakir Hossen M. Moazzam Hossen PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image Informatics in Medicine Unlocked Colon polyp Convolutional neural network (CNN) Self-attention Transfer learning Colonoscopy |
| title | PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image |
| title_full | PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image |
| title_fullStr | PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image |
| title_full_unstemmed | PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image |
| title_short | PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image |
| title_sort | polynet a self attention based cnn model for classifying the colon polyp from colonoscopy image |
| topic | Colon polyp Convolutional neural network (CNN) Self-attention Transfer learning Colonoscopy |
| url | http://www.sciencedirect.com/science/article/pii/S2352914825000425 |
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