A structured mathematical approach to improving gastrointestinal disease classification with transfer learning
Rakesh Sharmasrak.911@gmail.comDepartment of Computer Science and Engineering Manipal University JaipurDepartment of Computer Science Engineering Vivekanand Global UniversityJaipur, Rajasthan, 303012, IndiaView full profile → , *C. S. LambaCorresponding authorcs.lamba@jaipur.manipal.eduDepartment of Computer Science and Engineering Manipal University JaipurDepartment of Computer Science & Engineering Manipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 17 Dec 2024
- Published Online:
- 30 Sep 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIM-2348
- Pages:
- 2071–2078
Abstract
Keywords
Subject Classifications
References
[1] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 770–778 (2016).
[2] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. Int. Conf. Mach. Learn. (ICML), pp. 6105–6114 (2019).
[3] Y. Tian, L. Z. C. T. Pu, Y. Liu, G. Maicas, J. W. Verjans, A. D. Burt, S. H. Shin, R. Singh, and G. Carneiro, “Detecting, localising and classifying polyps from colonoscopy videos using deep learning,” arXiv preprint arXiv:2101.03285 (2021).
[4] A. Dosovitskiy, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16×16 words: Transformers for image recognition at scale,” in Proc. Int. Conf. Learn. Represent. (ICLR), vol. 1, no. 2 (2021).
[5] L. Wu, J. Gao, Y. Zhang, B. Sui, Y. Wen, Q. Wu, Y. Duan, and X. Bo, “A hybrid deep forest-based method for predicting synergistic drug combinations,” Cell Reports Methods, vol. 3, no. 2 (2023).
[6] M. Zhang, M. Li, J. Zhou, Y. Zhu, S. Wang, D. Liang, W. Wu, and Q. Liu, “High-dimensional embedding network derived prior for compressive sensing MRI reconstruction,” Med. Image Anal., vol. 64, pp. 101717 (2020).
[7] T. A. Gahwera, “Transfer learning-based ensemble approach for rainfall class amount prediction,” IEEE Access (2025).
[8] Y. Fontenla-Seco, M. Lama, V. González-Salvado, C. Pena-Gil, and A. Bugarín-Diz, “A framework for the automatic description of healthcare processes in natural language: Application in an aortic stenosis integrated care process,” J. Biomed. Inform., vol. 128, pp. 104033 (2022).
[9] T. W. Arega, S. Bricq, F. Legrand, A. Jacquier, A. Lalande, and F. Meriaudeau, “Automatic uncertainty-based quality controlled T1 mapping and ECV analysis from native and post-contrast cardiac T1 mapping images using Bayesian vision transformer,” Med. Image Anal., vol. 86, pp. 102773 (2023).
[10] S. Al-Otaibi, A. Rehman, M. Mujahid, S. Alotaibi, and T. Saba, “Efficient-Gastro: Optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images,” PeerJ Comput. Sci., vol. 10, pp. e1902 (Mar. 2024), doi: 10.7717/peerj-cs.1902.
[11] S. Wu, R. Zhang, J. Yan, C. Li, Q. Liu, L. Wang, and H. Wang, “High-speed and accurate diagnosis of gastrointestinal disease: Learning on endoscopy images using lightweight transformer with local feature attention,” Bioengineering, vol. 10, pp. 1416 (2023). doi: 10.3390/bioengineering10121416.
[12] B. Devi, S. Srivastava, V. K. Verma, and G. Aggarwal, “Transfer learning based novel intelligent classification for Alzheimer’s dementia using duplex convolutional neural network,” J. Discrete Math. Sci. Cryptogr., vol. 26, no. 6, pp. 1367–1379 (Sep. 2023).
