Mathematical Modelling for Generalized Lower Gastrointestinal Image Based Classification and Detection on Both ML and DL Techniques
Mathematical Modelling for Generalized Lower Gastrointestinal Image Based Classification and Detection on Both ML and DL Techniques |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-1 |
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| Year of Publication : 2025 | ||
| Author : S. Vasudevan, Vediyappan Govindan, Haewon Byeon |
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| DOI : 10.14445/22315381/IJETT-V73I1P118 | ||
How to Cite?
S. Vasudevan, Vediyappan Govindan, Haewon Byeon, "Mathematical Modelling for Generalized Lower Gastrointestinal Image Based Classification and Detection on Both ML and DL Techniques," International Journal of Engineering Trends and Technology, vol. 73, no. 1, pp. 207-224, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I1P118
Abstract
In this paper, the authors investigated an essential diagnostic and therapeutic technique for inspecting the colon, the distal portion of the small intestine, and the rectum in lower Gastrointestinal (GI) endoscopy. The condition of lower GI endoscopy today is thoroughly examined in this study, including its methods, uses, difficulties, and new developments. This research delves into the development of lower gastrointestinal endoscopy, emphasizing technological breakthroughs, improved procedural techniques, and its growing significance in medical practice. This review delves into the diagnostic potential of lower gastrointestinal endoscopy, highlighting its efficacy in identifying pathologies such as polyps, inflammatory bowel disorders, and colorectal malignancies. Discussion is held about the difficulties of lower GI endoscopy, such as patient pain, problems, and visual impairments. We examine ways to overcome these obstacles, including better sedation methods, better endoscope designs, and innovations in healthcare professional training. The study also discusses current technical advancements to improve lesion detection efficiency and accuracy, such as merging computer-aided detection techniques with artificial intelligence. Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) are investigated in this research for classification, lesion identification, and real-time picture processing during lower GI endoscopy. Furthermore, integrating sophisticated computer vision methods, such as feature extraction and picture segmentation, are examined to improve the visualization and comprehension of gastrointestinal diseases.
Keywords
Python, Machine Learning models, Deep Learning models, Gastrointestinal.
References
[1] Hyuna Sung et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians, The Flagship Journal of the American Sociaty, vol. 71, no. 3, pp. 209-249, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Patsy Gerstner, “The American Society for Gastrointestinal Endoscopy: A History,” Gastrointestinal Endoscopy, vol. 37, no. S2, pp. S1-S26, 1991.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Kenneth K. Wang, and Richard E. Sampliner, “Updated Guidelines 2008 For the Diagnosis, Surveillance and Therapy of Barrett’s Esophagus,” American Journal of Gastroenterology, vol. 103, no. 3, pp. 788-797, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Dev Gupta et al., “Classification of Endoscopic Images and Identification of Gastrointestinal Diseases,” 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), Faridabad, India, pp. 231-235, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas “Effective Voting of Heterogeneous Classifiers,” 15th European Conference on Machine Learning, Pisa, Italy, pp. 465-476, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[6] A. Srujan et al., “Detecting Cancer in Gastrointestinal Images Using MATLAB,” 2021 5th International Conference on Trends in Electronics and Informatics, Tirunelveli, India, pp. 1517-1521, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Debesh Jha et al., “A Comprehensive Analysis of Classification Methods in Gastrointestinal Endoscopy Imaging,” Medical Image Analysis, vol. 70, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Pia H. Smedsrud et al., “Kvasir-Capsule, A Video Capsule Endoscopy Dataset,” Scientific Data, vol. 8, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Yaw Afriyie, Benjamin A. Weyori, and Alex A. Opoku, “Gastrointestinal Tract Disease Recognition Based on Denoising Capsule Network,” Cogent Engineering, vol. 9, no. 1, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] E. Mossotto et al, “Classification of Paediatric Inflammatory Bowel Disease Using Machine Learning,” Scientific Reports, vol. 7, pp. 1-10, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton, “Dynamic Routing Between Capsules,” Advances in Neural Information Processing Systems, vol. 30, 2017.
[Google Scholar] [Publisher Link]
[12] Melaku Bitew Haile et al., “Detection and Classification of Gastrointestinal Disease Using Convolutional Neural Network and SVM,” Cogent Engineering, vol. 9, no. 1, pp. 1-23, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Hanna Borgli et al., “HyperKvasir, A Comprehensive Multi-Class Image and Video Dataset for Gastrointestinal Endoscopy,” Scientific Data, vol 7, no. 1, pp. 1-14, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Muhammad Sharif et al., “Deep CNN and Geometric Features-Based Gastrointestinal Tract Diseases Detection and Classification from Wireless Capsule Endoscopy Images,” Journal of Experimental and Theoretical Artificial Intelligence, vol. 33, no. 4, pp. 577-599, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Yi Wang et al., “Polyp-Alert: Near Real-Time Feedback During Colonoscopy,” Computer Methods and Programs in Biomedicine, vol. 120, no. 3, pp. 164-179, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Baopu Li, and Max Q.-H. Meng, “Tumor Recognition in Wireless Capsule Endoscopy Images Using Textural Features and SVM-Based Feature Selection,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 323-329, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Franco Scarselli et al., “The Graph Neural Network Model,” IEEE Transactions on Neural Networks, vol. 20, no. 1, pp. 61-80, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Tsung-Han Chan et al., “PCANet: A Simple Deep Learning Baseline for Image Classification?,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5017-5032, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Xinghao Yang et al., “Canonical Correlation Analysis Networks for Two-View Image Recognition,” Information Sciences, vol. 385-386, pp. 338-352, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Simon Andermatt, Simon Pezold, and Philippe Cattin, “Multi-Dimensional Gated Recurrent Units for The Segmentation of Biomedical 3D-Data,” Deep Learning and Data Labeling for Medical Applications, Athens, Greece, pp. 142-151, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Y. Bengio, P. Simard, and P. Frasconi, “Learning Long-Term Dependencies with Gradient Descent Is Difficult,” IEEE Transactions on Neural Networks, vol. 5, no. 2, pp. 157-166, 1994.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Xinting Gao, Stephen Lin, and Tien Yin Wong, “Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2693-2701, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Jie Zhou et al, “Graph Neural Networks: A Review of Methods and Applications,” AI Open, vol. 1, pp. 57-81, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Serdar Durak et al., “Deep Neural Network Approaches for Detecting Gastric Polyps in Endoscopic Images,” Medical and Biological Engineering and Computing, vol. 59, pp. 1563-1574, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Pradipta Sasmal et al., “An Automated Framework for Detection, Localization, and Classification of Colonic Polyp Using Deep Learning,” Research Square, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Tao Yan et al., “Semantic Segmentation of Gastric Polyps in Endoscopic Images Based on Convolutional Neural Networks and an Integrated Evaluation Approach,” Bioengineering, vol. 10, no. 7, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Iria Varela-Rey et al., “Design and Biopharmaceutical Preclinical Characterisation of a New Thermosensitive Hydrogel for The Removal of Gastric Polyps,” International Journal of Pharmaceutics, vol. 635, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Sara Massironi et al., “Exploring the Spectrum of Incidental Gastric Polyps in Autoimmune Gastritis,” Digestive and Liver Disease, vol. 55, no. 9, pp. 1201-1207, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Junjun Huang et al., “A Masked Graph Neural Network Model for Real-Time Gastric Polyp Detection in Healthcare 4.0,” Journal of Industrial Information Integration, vol. 34, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Farhana Yasmin et al., “GastroNet: Gastrointestinal Polyp and Abnormal Feature Detection and Classification with Deep Learning Approach,” IEEE Access, vol. 11, pp. 97605-97624, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Mark Sandler et al., “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 4510-4520, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Tsung-Yi Lin et al., “Focal Loss for Dense Object Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 318-327, 2020.
[CrossRef] [Google Scholar] [Publisher Link]