Citation

Eabne Delowar, Khaled and Uddin, Mohammed Borhan and Khaliluzzaman, Md and Rabbi, Riadul Islam and Hossen, Md Jakir and Hossen, M. Moazzam (2025) PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image. Informatics in Medicine Unlocked, 56. p. 101654. ISSN 23529148

Text PolyNet_ A self-attention based CNN model for classifying the colon polyp from colonoscopy image.pdf - Published Version Restricted to Repository staff only Download (9MB)

Abstract

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 convolu tional neural network (CNN) model for correctly predicting colon polyps using the Kvasir dataset. The selfattention 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.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network (CNN)
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 May 2025 02:35
Last Modified:	30 May 2025 02:35
URII:	http://shdl.mmu.edu.my/id/eprint/13894

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