IRO Journals
Journal of Innovative Image Processing

An Improved ResNet-Based Deep Learning Model with Modified Mountain Gazelle Optimization for Gastrointestinal Disease Classification

Open Access
Published: 31 July, 2026
Pages: 1266-1289
  • Harshitha E.S. , Harshitha E.S.
    Research Scholar, Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Pulloor, Kariapatti, India
    Research Scholar, Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Pulloor, Kariapatti, India
  • Narmadha G. , Narmadha G.
    Professor, Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Pulloor, Kariapatti, India
    Professor, Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Pulloor, Kariapatti, India
  • Sakthivel B. , Sakthivel B.
    Professor, Department of Electronics and Communication Engineering, Pandian Saraswathi Yadav Engineering College, Sivagangai, India
    Professor, Department of Electronics and Communication Engineering, Pandian Saraswathi Yadav Engineering College, Sivagangai, India
  • Muneeswari B. Muneeswari B.
    Professor, Department of Electronics and Communication Engineering, Velammal College of Engineering and Technology, Madurai, India
    Professor, Department of Electronics and Communication Engineering, Velammal College of Engineering and Technology, Madurai, India
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How to Cite

E.S., Harshitha, Narmadha G., Sakthivel B., and Muneeswari B. 2026. “An Improved ResNet-Based Deep Learning Model With Modified Mountain Gazelle Optimization for Gastrointestinal Disease Classification”. Journal of Innovative Image Processing 8 (3): 1266-89. https://doi.org/10.36548/jiip.2026.3.024.
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Keywords

Gastrointestinal Tract Diseases
Deep Learning
Improved ResNet
Channel-Spatial Context Gating
Mountain Gazelle Optimizer

Abstract

Gastrointestinal (GI) tract diseases occur due to abnormalities affecting different regions of the digestive system. Diagnosing GI tract diseases typically involves methods such as endoscopy, imaging examinations, biopsy analysis, and clinical assessments. The severity and stage of the disease play a crucial role in determining the appropriate treatment plan. Medical imaging techniques are used to identify abnormal regions and assess the progression of diseases. The categorization of GI tract diseases, including ulcerative lesions, polyps, bleeding regions, inflammatory conditions, and normal tissues, is crucial for accurate diagnosis and effective treatment planning. Recently, Artificial Intelligence (AI)-based Deep Learning (DL) models have received greater attention due to their accuracy and flexibility. In this work, a threefold DL model is suggested to classify GI tract disease types. An improved ResNet, called Channel-Spatial Context Gating ResNet (CSCG-ResNet), is proposed for efficient feature extraction. Then, a modified Mountain Gazelle Optimizer (MGO) is proposed for feature optimization. Finally, the Quantum-inspired TabNet is proposed for multi-class categorization. The performance of the model is validated using the Kvasir dataset. The model achieves an overall accuracy of 96.75% when compared to previously proposed models.

References

  1. Mukhtorov, Doniyorjon, Madinakhon Rakhmonova, Shakhnoza Muksimova, and Young-Im Cho. "Endoscopic Image Classification Based on Explainable Deep Learning." Sensors 23, no. 6 (2023): 3176.
  2. Li, Sheng, Jing Cao, Jiafeng Yao, Jinhui Zhu, Xiongxiong He, and Qianru Jiang. "Adaptive Aggregation with Self‐Attention Network for Gastrointestinal Image Classification." IET Image Processing 16, no. 9 (2022): 2384-2397.
  3. Asif, Sohaib. "A Fuzzy Minkowski Distance-Based Fusion of Convolutional Neural Networks for Gastrointestinal Disease Detection." Applied Soft Computing 158 (2024): 111595.
  4. Khan, Muhammad Attique, Seifedine Kadry, Majed Alhaisoni, Yunyoung Nam, Yudong Zhang, Venkatesan Rajinikanth, and Muhammad Shahzad Sarfraz. "Computer-Aided Gastrointestinal Diseases Analysis from Wireless Capsule Endoscopy: A Framework of Best Features Selection." IEEE Access 8 (2020): 132850-132859.
  5. Naz, Javeria, Muhammad Sharif, Mussarat Yasmin, Mudassar Raza, and Muhammad A. Khan. "Detection and Classification of Gastrointestinal Diseases Using Machine Learning." Current Medical Imaging Reviews 17, no. 4 (2021): 479-490.
  6. Öztürk, Şaban, and Umut Özkaya. "Gastrointestinal Tract Classification Using Improved LSTM based CNN." Multimedia Tools and Applications 79, no. 39 (2020): 28825-28840.
  7. Ramzan, Muhammad, Mudassar Raza, Muhammad Sharif, Muhammad Attique Khan, and Yunyoung Nam. "Gastrointestinal Tract Infections Classification Using Deep Learning." Comput. Mater. Contin 69, no. 3 (2021): 3239-3257.
  8. Rahman, AMJ MD Zubair, R. Mythili, K. Chokkanathan, T. R. Mahesh, K. Vanitha, and Temesgen Engida Yimer. "Enhancing Image-Based Diagnosis of Gastrointestinal Tract Diseases Through Deep Learning with EfficientNet and Advanced Data Augmentation Techniques." BMC Medical Imaging 24 (2024): 1.
  9. Haile, Melaku Bitew, Ayodeji Olalekan Salau, Belay Enyew, and Abebech Jenber Belay. "Detection and Classification of Gastrointestinal Disease Using Convolutional Neural Network and SVM." Cogent Engineering 9, no. 1 (2022): 2084878.
  10. Rubab, Saddaf, Muhammad Jamshed, Muhammad Attique Khan, Nouf Abdullah Almujally, Robertas Damaševičius, Amir Hussain, Neunggyu Han, and Yunyoung Nam. "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, no. 1 (2025): 32180.
  11. Nouman Noor, Muhammad, Muhammad Nazir, Sajid Ali Khan, Imran Ashraf, and Oh-Young Song. "Localization and Classification of Gastrointestinal Tract Disorders Using Explainable AI from Endoscopic Images." Applied Sciences 13, no. 15 (2023): 9031.
  12. Mohapatra, Subhashree, Girish Kumar Pati, Manohar Mishra, and Tripti Swarnkar. "Gastrointestinal Abnormality Detection and Classification Using Empirical Wavelet Transform and Deep Convolutional Neural Network from Endoscopic Images." Ain Shams Engineering Journal 14, no. 4 (2023): 101942.
  13. Ahamed, Md Faysal, Md Nahiduzzaman, Md Rabiul Islam, Mansura Naznine, Mohamed Arselene Ayari, Amith Khandakar, and Julfikar Haider. "Detection of Various Gastrointestinal Tract Diseases Through a Deep Learning Method with Ensemble ELM and Explainable AI." Expert Systems with Applications 256 (2024): 124908.
  14. Şener, Abdullah, and Burhan Ergen. "Automatic Detection of Gastrointestinal System Abnormalities Using Deep Learning-Based Segmentation and Classification Methods." Health Information Science and Systems 13, no. 1 (2025): 37.
  15. Sivari, Esra, Erkan Bostanci, Mehmet Serdar Guzel, Koray Acici, Tunc Asuroglu, and Tulin Ercelebi Ayyildiz. "A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models." Diagnostics 13, no. 4 (2023): 720.
  16. Tanwar, Vishesh, Bhisham Sharma, Dhirendra Prasad Yadav, and Abolfazl Mehbodniya. "Hybrid Deep Learning Framework Based on EfficientViT for Classification of Gastrointestinal Diseases." Scientific Reports 15, no. 1 (2025): 26982.
  17. Khan, Sultan Daud, Saleh Basalamah, and Ahmed Lbath. "Multi-Module Attention-Guided Deep Learning Framework for Precise Gastrointestinal Disease Identification in Endoscopic Imagery." Biomedical Signal Processing and Control 95 (2024): 106396.
  18. Abian, Arefin Ittesafun, Mohaimenul Azam Khan Raiaan, Mirjam Jonkman, Sheikh Mohammed Shariful Islam, and Sami Azam. "Atrous Spatial Pyramid Pooling with Swin Transformer Model for Classification of Gastrointestinal Tract Diseases from Videos with Enhanced Explainability." Engineering Applications of Artificial Intelligence 150 (2025): 110656.
  19. Hussein, Ahmed F., Auns Q. Al-Neami, and Noor Kamal Al-Qazzaz. "Transfer Learning and Hybrid Deep Convolutional Neural Networks for Detection and Classification of Gastrointestinal Diseases." International Journal of Information Technology (2025): 1-15.
  20. Öztürk, Şaban, and Umut Özkaya. "Residual LSTM Layered CNN for Classification of Gastrointestinal Tract Diseases." Journal of Biomedical Informatics 113 (2021): 103638.
  21. Gunasekaran, Hemalatha, Krishnamoorthi Ramalakshmi, Deepa Kanmani Swaminathan, and Manuel Mazzara. "GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images." Bioengineering 10, no. 7 (2023): 809.
  22. Majid, Abdul, Muhammad Sharif, and Mehwish Zafar. "A Lightweight Attention-Based Framework with Multi-Scale Deep Supervision and Boundary-Aware Learning for Gastrointestinal Tract Image Segmentation." Signal, Image and Video Processing 20, no. 2 (2026): 78.
  23. Siddiqui, Samra, Junaid A. Khan, Tallha Akram, Meshal Alharbi, Jaehyuk Cha, and Dina A. AlHammadi. "SNet: A Novel Convolutional Neural Network Architecture for Advanced Endoscopic Image Classification of Gastrointestinal Disorders." SLAS technology 33 (2025): 100304.
  24. https://datasets.simula.no/kvasir/
  25. Rauf, Rehana, Samia Ijaz, Saima Gulzar Ahmad, Ehsan Ullah Munir, and Naeem Ramzan. "Gastrointestinal Endoscopic Image Classification Using a Hybrid Modified Inception Network and a Customized Vision Transformer with Tree Growth Feature Selection." Scientific Reports (2026).