Classification of Gastrointestinal Diseases in Endoscopic Images: Comparative Analysis of Convolutional Neural Networks and Vision Transformers

Year 2024, Volume: 14 Issue: 3, 988 - 999, 01.09.2024

Enes Ayan

https://doi.org/10.21597/jist.1501787

Abstract

Gastrointestinal (GI) diseases are a major issue in the human digestive system. Therefore, many studies have explored the automatic classification of GI diseases to reduce the burden on clinicians and improve patient outcomes for both diagnosis and treatment purposes. Convolutional neural networks (CNNs) and Vision Transformers (ViTs) in deep learning approaches have become a popular research area for the automatic detection of diseases from medical images. This study evaluated the classification performance of thirteen different CNN models and two different ViT architectures on endoscopic images. The impact of transfer learning parameters on classification performance was also observed. The tests revealed that the classification accuracies of the ViT models were 91.25% and 90.50%, respectively. In contrast, the DenseNet201 architecture, with optimized transfer learning parameters, achieved an accuracy of 93.13%, recall of 93.17%, precision of 93.13%, and an F1 score of 93.11%, making it the most successful model among all the others. Considering the results, it is evident that a well-optimized CNN model achieved better classification performance than the ViT models.

Keywords

Medical Image Classification, Convolutional Neural Networks, Vision Transformers, Fine Tuning, Transfer Learning, Gastrointestinal Diseases

References

• Agrawal, T., Gupta, R., & Narayanan, S. (2019). On evaluating CNN representations for low resource medical image classification. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1363–1367.
• Chai, J., Zeng, H., Li, A., & Ngai, E. W. T. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6, 100134.
• Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. arXiv preprint arXiv:1610.02357.
• Demirbaş, A. A., Üzen, H., & Fırat, H. (2024). Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset. Health Information Science and Systems, 12(1), 32.
• Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale arXiv preprint arXiv:2010.11929.
• Gjestang, H. L., Hicks, S. A., Thambawita, V., Halvorsen, P., & Riegler, M. A. (2021). A self-learning teacher-student framework for gastrointestinal image classification. IEEE International Symposium on Computer-Based Medical Systems (CBMS), 539–544.
• He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv preprint arXiv:1512.03385.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity Mappings in Deep Residual Networks. arXiv preprint arXiv:1603.05027.
• Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q. V., & Adam, H. (2019). Searching for MobileNetV3 (arXiv:1905.02244).
• Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2018). Densely Connected Convolutional Networks (arXiv:1608.06993).
• Huo, X., Tian, S., Yang, Y., Yu, L., Zhang, W., & Li, A. (2024). SPA: Self-Peripheral-Attention for central–peripheral interactions in endoscopic image classification and segmentation. Expert Systems with Applications, 245, 123053.
• Karaman, A., Karaboga, D., Pacal, I., Akay, B., Basturk, A., Nalbantoglu, U., Coskun, S., & Sahin, O. (2023). Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection. Applied Intelligence, 53(12), 15603–15620.
• Katai, H., Ishikawa, T., Akazawa, K., Isobe, Y., Miyashiro, I., Oda, I., Tsujitani, S., Ono, H., Tanabe, S., Fukagawa, T., Nunobe, S., Kakeji, Y., & Nashimoto, A. (2018). Five-year survival analysis of surgically resected gastric cancer cases in Japan: A retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001–2007). Gastric Cancer, 21(1), 144–154.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25.
• Leufkens, A., Van Oijen, M., Vleggaar, F., & Siersema, P. (2012). Factors influencing the miss rate of polyps in a back-to-back colonoscopy study. Endoscopy, 44(05), 470–475.
• Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999–7019.
• Lonseko, Z. M., Adjei, P. E., Du, W., Luo, C., Hu, D., Zhu, L., Gan, T., & Rao, N. (2021). Gastrointestinal disease classification in endoscopic images using attention-guided convolutional neural networks. Applied Sciences, 11(23), 11136.
• Mukhtorov, D., Rakhmonova, M., Muksimova, S., & Cho, Y.-I. (2023). Endoscopic image classification based on explainable deep learning. Sensors, 23(6), 3176.
• Pacal, I. (2024). Improved Vision Transformer with Lion Optimizer for Lung Diseases Detection. International Journal of Engineering Research and Development, 16(2), 760-776.
• Pogorelov, K., Randel, K. R., Griwodz, C., Eskeland, S. L., De Lange, T., Johansen, D., Spampinato, C., Dang-Nguyen, D.-T., Lux, M., Schmidt, P. T., Riegler, M., & Halvorsen, P. (2017). KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection. Proceedings of the 8th ACM on Multimedia Systems Conference, 164–169.
• Ribani, R., & Marengoni, M. (2019). A survey of transfer learning for convolutional neural networks. SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), 47–57.
• Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2019). MobileNetV2: Inverted Residuals and Linear Bottlenecks. arXiv preprint arXiv:1801.04381.
• Sermet, F., & Pacal, I. (2024). Deep learning approaches for autonomous crack detection in concrete wall, brick deck and pavement. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 15(2), 503-513.
• Siddiqui, S., Akram, T., Ashraf, I., Raza, M., Khan, M. A., & Damaševičius, R. (2024). CG‐Net: A novel CNN framework for gastrointestinal tract diseases classification. International Journal of Imaging Systems and Technology, 34(3), e23081.
• Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556.
• Sivari, E., Bostanci, E., Guzel, M. S., Acici, K., Asuroglu, T., & Ercelebi Ayyildiz, T. (2023). A new approach for gastrointestinal tract findings detection and classification: Deep learning-based hybrid stacking ensemble models. Diagnostics, 13(4), 720.
• Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. arXiv preprint arXiv:1602.07261; Version 2.
• Yogapriya, J., Chandran, V., Sumithra, M. G., Anitha, P., Jenopaul, P., & Suresh Gnana Dhas, C. (2021). Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model. Computational and Mathematical Methods in Medicine, 2021, 1–12.
• Zhang, X., Chen, F., Yu, T., An, J., Huang, Z., Liu, J., Hu, W., Wang, L., Duan, H., & Si, J. (2019). Real-time gastric polyp detection using convolutional neural networks. PloS One, 14(3), e0214133.
• Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning Transferable Architectures for Scalable Image Recognition. arXiv preprint arXiv:1707.07012.