Authors:
K. Rajeswari,A. S. Salma Banu,P. Sunitha,P. Arivazhagan,DOI NO:
https://doi.org/10.26782/jmcms.2026.03.00004Keywords:
Gastrointestinal Disease,Stomach Ulcers,Deep Learning,Convolutional Neural Network,Empirical Mode Decomposition,Endoscopy Imaging.,Abstract
Timely intervention of gastrointestinal (GI) diseases, namely polyps, ulcerative colitis, and esophagitis are critical for improving the quality of human life and reducing the mortality rate associated with these conditions. Hence, early detection and diagnosis of GI disease are essential because they can reduce the severity of the disease. Traditional medical imaging techniques are time-intensive, labor-intensive, and susceptible to human error. Recently, deep learning models have been extensively used for image classification tasks, and they are consistently achieving promising results in real-time decision-making. However, the conventional deep learning models struggle with overfitting and poor generalization on medical imaging datasets because of the wide variability in disease types. To address this issue, a Hybrid Attention Convolutional Neural Network (HA-CNN) is proposed in this analysis. This proposed model integrates the strength of the convolutional operation and attention mechanism to focus on discriminative regions and features in medical images. The hybrid model is designed for high variability and complex features in the medical images. This model can accurately recognise lesion regions and detect types of diseases, and avoids overfitting. The effectiveness of the proposed HA-CNN is evaluated using a benchmark dataset, namely the Kvasir dataset, using 5-fold stratified cross-validation. The model achieves a mean classification accuracy of 94.43% ± 0.58, outperforming existing comparative methods. Moreover, the integration of empirical mode decomposition and dynamic scaling enhanced the quality of training data by improving the generalization ability of the model. By overcoming the existing challenges, this framework focuses on improving the diagnostic process in medical imaging, resulting in the precise detection of GI diseases.Refference:
I. Abbaszadegan MR, Tavasoli A, Velayati A, Sima HR, Vosooghinia H, Farzadnia M, Asadzedeh H, Gholamin M, Dadkhah E, Aarabi A. (2007). Stool-based DNA testing, a new noninvasive method for colorectal cancer screening, the first report from Iran. World J Gastroenterol. 13(10):1528-1533. 10.3748/wjg.v13.i10.1528.
II. Ajitha Gladis KP, Roja Ramani D, Mohana Suganthi N, Linu Babu. (2024). Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model. Technol Health Care. 32(6):4453-4473. doi: 10.3233/THC-240603.
III. Cambay VY, Barua PD, Hafeez Baig A, Dogan S, Baygin M, Tuncer T, Acharya UR. (2024). Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images. Sensors. 24. 10.3390/s24237710
IV. Demirba? AA, Üzen H, F?rat H. (2024). Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset. Health Inf. Sci. Syst.12(32). 10.1007/s13755-024-00290-x
V. D'Souza N, Brzezicki A, Abulafi M. (2019). Faecal immunochemical testing in general practice. Br J Gen Pract. 69(679): 60-61. 10.3399/bjgp19X700853.
VI. Gad E, Soliman S, Saeed Darweesh M. (2023). Advancing Brain Tumor Segmentation via Attention-Based 3D U-Net Architecture and Digital Image Processing. 12th International Conference on Model and Data Engineering At: Sousse, Tunisia. 10.1007/978-3-031-49333-1_18.
VII. Gupta D, Anand G, Kirar P, Meel P. (2022). 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. 231-235. doi: 10.1109/COM-IT-CON54601.2022.9850571.
VIII. Hong SM, Baek DH. (2023). A Review of Colonoscopy in Intestinal Diseases. Diagnostics. 13(7):1262. 10.3390/diagnostics13071262
IX. Huo X, Sun G, Tian S, Wang Y, Yu L, Long J, Zhang W, Li A. (2024). HiFuse: Hierarchical multi-scale feature fusion network for medical image classification. Biomed. Signal Process. Control. 87.
X. Jin D, Wen X, Wen Y. (2024). Personalized learning efficiency data analysis based on multi-scale convolution architecture and hybrid loss. Neural Comput & Applic. 36: 9753–9766. 10.1007/s00521-023-09099-3
XI. Kamble A, Bandodkar V, Dharmadhikary S, Anand V, Sanki PK, Wu MX, Jana B. (2025). Enhanced Multi-Class Classification of Gastrointestinal Endoscopic Images with Interpretable Deep Learning Model. arXiv:2503.00780v1.
XII. Korkmaz ?., Soygazi F. (2024). Gastrointestinal Image Classification Using Deep Learning Architectures via Transfer Learning. 2024 Medical Technologies Congress (TIPTEKNO), Mugla, Turkiye. 1-4. 10.1109/TIPTEKNO63488.2024.10755310.
XIII. Li X, Lei L, Sun Y, Li M, Kuang G. (2020). Multimodal Bilinear Fusion Network With Second-Order Attention-Based Channel Selection for Land Cover Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13: 1011-1026. 10.1109/JSTARS.2020.2975252.
XIV. Mang T, Graser A, Schima W, Maier A. (2007). CT colonography: Techniques, indications, findings. European Journal of Radiology. 61(3): 388-399. 10.1016/j.ejrad.2006.11.019
XV. Medical Advisory Secretariat. (2009). Flexible sigmoidoscopy for colorectal cancer screening: an evidence-based analysis. Ont Health Technol Assess Ser. 9(11):1-23.
XVI. Mukhtorov D, Rakhmonova M, Muksimova S, Cho Y-I. (2023). Endoscopic Image Classification Based on Explainable Deep Learning. Sensors. 23(6). 10.3390/s23063176
XVII. Nouman Noor M, Nazir M, Khan SA, Song O-Y, Ashraf I. (2023). Efficient Gastrointestinal Disease Classification Using Pretrained Deep Convolutional Neural Network. Electronics. 12(7):1557. 10.3390/electronics12071557
XVIII. Pal A, Rai HM, Frej MBH, Razaque A. (2024). Advanced Segmentation of Gastrointestinal (GI) Cancer Disease Using a Novel U-MaskNet Model. Life (Basel), 14(11). doi: 10.3390/life14111488.
XIX. Patel V, Patel K, Goel P, Shah M. (2024). Classification of Gastrointestinal Diseases from Endoscopic Images Using Convolutional Neural Network with Transfer Learning. In Proceedings of the 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Triunelveli, India. 504–508.
XX. Raut V, Gunjan R, Shete VV, Eknath UD. (2023). Gastrointestinal tract disease segmentation and classification in wireless capsule endoscopy using intelligent deep learning model. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 11(3): 606–622. 10.1080/21681163.2022.2099298
XXI. Rubab S, Jamshed M, Khan MA, Almujally NA, Damaševi?ius R, Hussain A, Han N, Nam Y. (2025). Gastrointestinal tract disease classification from wireless capsule endoscopy images based on deep learning information fusion and Newton Raphson controlled marine predator algorithm. Scientific Reports. 15(32180). 10.1038/s41598-025-17204-w
XXII. Saba A, Amin J, Ali MU. (2025). Deep Q-Learning for Gastrointestinal Disease Detection and Classification. Bioengineering. 12(11). 10.3390/bioengineering12111184
XXIII. Shekokar NM, Vasudevan H, Durbha SS, Michalas A, Nagarhalli TP. (2023). Intelligent Approaches to Cyber Security (1st ed.). Chapman and Hall/CRC. 10.1201/9781003408307
XXIV. Wang C, Wang Y, Liu Y, He Z, He R, Sun Z. (2020). ScleraSegNet: An Attention Assisted U-Net Model for Accurate Sclera Segmentation. IEEE Transactions on Biometrics, Behavior and Identity Science. 2(1): 40-54. 10.1109/TBIOM.2019.2962190.
XXV. Xu C, Shu J, Wang Z, Wang J. (2024). A Scene Classification Model Based on Global-Local Features and Attention in Lie Group Space. Remote Sensing. 16(13):2323. 10.3390/rs16132323
XXVI. Yang J, Park K. (2024). Improving Gait Analysis Techniques with Markerless Pose Estimation Based on Smartphone Location. Bioengineering. 11(2). 10.3390/bioengineering11020141
XXVII. Yogapriya J, Chandran V, Sumithra MG, Anitha P, Jenopaul P, Suresh Gnana Dhas C. (2021). Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model. Comput Math Methods Med. 10.1155/2021/5940433.
XXVIII. Zubair Rahman AMJMD, Mythili R, Chokkanathan K, Mahesh TR, Vanitha K, Yimer TE. (2024). Enhancing image-based diagnosis of gastrointestinal tract diseases through deep learning with EfficientNet and advanced data augmentation techniques. BMC Med Imaging. 24(306). 10.1186/s12880-024-01479-y