International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Empirical Study on Gastrointestinal Disease Diagnosis for Ulcer Detection with KVASIR Dataset |
|---|---|
| ABSTRACT | Worldwide health concern has Gastrointestinal (GI) diseases that affect million people annually. GI diseases are ulcers in rigorous fitness complications when analyzed at earlier stage. At gastroenterology, Wireless capsule endoscopy (WCE) is utilized to examine GI area. WCE collects the images for performing GI disease diagnosis. Image pre-processing, feature extraction and classification are carried out for efficient GI disease diagnosis. The automated diagnosis systems collected the WCE images and find the GI disease in automatic way. The computer-aided algorithms extracted image features like texture, color, and shape for performing efficient image classification. But, traditional approaches failed to improve accuracy and lesser time. In order to address these issues, gastrointestinal illness analysis was carried out by employing ML as well as DL. |
| AUTHOR | BHUVANESWARI .S, M.SULTHAN IBRAHIM Part Time Research Scholar, Department of Computer Application, Madurai Kamaraj University, Veerapandi, Theni, Tamil Nadu, India Head & Assistant Professor, PG Department of Computer Science, Government Arts and Science College- Affliated to Madurai Kamaraj University, Veerapandi, Theni, Tamil Nadu, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403025 |
| pdf/25_Empirical Study on Gastrointestinal Disease Diagnosis for Ulcer.pdf | |
| KEYWORDS | |
| References | [1] Arefin Ittesafun Abian, 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, Elsevier, Volume 150,15 June 2025, Pages 1-15 [2] P. Linu Babu and S. Jana, “Gastrointestinal tract disease detection via deep learning based Duo-Feature Optimized Hexa-Classification model”, Biomedical Signal Processing and Control, Elsevier, Volume 100, Part C, February 2025, Pages 1-15 [3] Zahid Farooq Khan, Muhammad Ramzan, Mudassar Raza, Muhammad Attique Khan, Khalid Iqbal, Taerang Kim and Jae-Hyuk Cha, “Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes”, Computers, Materials and Continua, Elsevier, Volume 78, Issue 1, 30 January 2024, Pages 1207-1225 [4] Samra Siddiqui, 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, Elsevier, Volume 33, August 2025, Pages 1-5 [5] Zafran Waheed, Jinsong Gui, Kamran Amjad, Ikram Waheed and Sohaib Asif, “An ensemble approach of deep CNN models with Beta normalization aggregation for gastrointestinal disease detection”, Biomedical Signal Processing and Control, Elsevier, Volume 105, July 2025, Pages 1-15 [6] Sohaib Asif, Ming Zhao, Fengxiao Tang and Yusen Zhu, “DCDS-Net: Deep transfer network based on depth-wise separable convolution with residual connection for diagnosing gastrointestinal diseases”, Biomedical Signal Processing and Control, Elsevier, Volume 90, April 2024, Pages 1-15 [7] Marwa Obayya, Fahd N. Al-Wesabi, Mashael Maashi, Abdullah Mohamed, Manar Ahmed Hamza, Suhanda Drar, Ishfaq Yaseen, And Mohamed Ibrahim Alsaid, “Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images”, IEEE Access, Volume 11, 2023, Pages 25959 – 25967 [8] Md. Faysal Ahamed, Md. Nahiduzzaman, Md. Rabiul Islam, Mansura Naznine, Mohamed Arselene Ayari, Amith Khandakar, Julfikar Haider, “Detection of various gastrointestinal tract diseases through a deep learning method with ensemble ELM and explainable AI”, Expert Systems with Applications, Elsevier, Volume 256, 2024, Pages 1-31 [9] Abdullah Şener and Burhan Ergen, “Automatic detection of gastrointestinal system abnormalities using deep learning-based segmentation and classification methods”, Health Information Science and Systems, Springer, Volume 13, 2025, Pages 1-25 [10] Yijie Zhu, Xiaoguang Lyu, Xiao Tao, Lianlian Wu, Anning Yin, Fei Liao, Shan Hu, Yang Wang, Mengjiao Zhang, Li Huang, Junxiao Wang, Chenxia Zhang, Dexin Gong, Xiaoda Jiang, Liang Zhao And Honggang Yu, “A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination”, BMC Gastroenterology, Springer, Volume 24, 2024, Pages 1-9. [11] Bo Ye, Zhi Shu, Bo Wang, Shufang Wang, Yingbing Fu and Leting Zhang, “Attention Mechanism Guided SE + ResNet-H Model for Gastrointestinal Endoscopy Image Classification”, IEEE Transactions on Instrumentation and Measurement, Volume 73, March 2024, Pages 1-15 [12] Fahdah A Almarshad, Prasanalakshmi Balaji, Liyakathunisa Syed, Eman Aljohani, Santhi Muttipoll Dharmarajlu and Thavavel Vaiyapuri, “An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification”, IEEE Access, Volume 12, August 2024, Pages 137237 – 137246 [13] Erdal Ozbay, “Gastrointestinal Tract Disease Classification Using Residual-Inception Transformer with Wireless Capsule Endoscopy Images Segmentation”, IEEE Access, Volume 12, December 2024, Pages 197988 – 197998 [14] Ahmed S. Almasoud, Mohammed Maray, Hend Khalid Alkahtani, Faiz Abdullah Alotaibi, Mrim M. Alnfiai and Ahmed Sayed, “Gastrointestinal Cancer Detection and Classification Using African Vulture Optimization Algorithm with Transfer Learning”, IEEE Access, Volume 12, January 2024, Pages 23122 – 23131 [15] Han Guo, Sai Ashish Somayajula, Ramtin Hosseini and Pengtao Xie, “Improving image classification of gastrointestinal endoscopy using curriculum self-supervised learning”, Scientific Reports, Volume 14, Issue 6100, 2024, Pages 1-15 [16] Abdulrahman Alruban, Eatedal Alabdulkreem, Majdy M. Eltahir, Abdullah R Alharbi, Iméne Issaoui and Ahmed Sayed, “Endoscopic Image Analysis for Gastrointestinal Tract Disease Diagnosis Using Nature Inspired Algorithm with Deep Learning Approach”, IEEE Access, Volume 11, November 2023, Pages 130022 - 130030 [17] Rui Gong MM, Bing Yue, Xiujing Sun, Shutian Zhang, Peng Li and Rui Cheng, “Magnifying endoscopy with novel image enhancement technology for gastrointestinal tumors”, iGIE, Elsevier, June 2025, Pages 1-15 [18] Bilin Wang, Changda Lei, Kaicheng Hong, Xiuji Kan, Yifan Ouyang, Junbo Li, Yunbo Guo and Rui Li, “Diffusion-based image translation from white light to narrow-band imaging in gastrointestinal endoscopy”, Computerized Medical Imaging and Graphics, Elsevier, Volume 124, September 2025, Pages 1-15 [19] Anju Sharma, Rajnish Kumar and Prabha Garg, “Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images”, International Journal of Medical Informatics, Elsevier, Volume 177, September 2023, Pages 1-15 [20] Cheng Zhou, Jinlei Jiang, Songwei Huang, Junhao Wang, Xinyuan Cui, Weicheng Wang, Mingrui Chen, Jiawei Peng, Nanqing Shi, Bensong Wang, Amin Zhang, Qian Zhang, Qichao Li, Shengsheng Cui, Shenghao Xue, Wei Wang, Ning Tang and Daxiang Cui, “An ingestible near-infrared fluorescence capsule endoscopy for specific gastrointestinal diagnoses”, Biosensors and Bioelectronics, Elsevier, Volume 257, August 2024, Pages 1-15 [21] Bong Hyuk Jeong, Hang Keun Kim and Young Don Son, “Depth estimation from monocular endoscopy using simulation and image transfer approach”, Computers in Biology and Medicine, Elsevier, Volume 181, October 2024, Pages 1-15 [22] Deepak Bajhaiya and Sujatha Narayanan Unni, “Deep learning-enabled detection and localization of gastrointestinal diseases using wireless-capsule endoscopic images”, Biomedical Signal Processing and Control, Elsevier, Volume 93, July 2024, Pages 1-18 [23] V. Sharmila and S. Geetha, “Gastro Intestinal Disease Classification Using Hierarchical Spatio Pyramid TranfoNet with PitTree Fusion and Efficient-CondConv SwishNet”, IEEE Access, Volume 12, Pages 113972 – 113987 [24] Erdal Ozbay, “Gastrointestinal Tract Disease Classification Using Residual-Inception Transformer with Wireless Capsule Endoscopy Images Segmentation”, IEEE Access, Volume 12, December 2024, Pages 197988 – 197998 [25] Dimitris K. Iakovidis, Spiros V. Georgakopoulos, Michael Vasilakakis, Anastasios Koulaouzidis and Vassilis P. Plagianakos, “Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification”, IEEE Transactions on Medical Imaging, Volume 37, Issue 10, October 2018, Pages 2196 - 2210 [26] Wei Wang, Xin Yang and Jinhui Tang, “Vision Transformer with Hybrid Shifted Windows for Gastrointestinal Endoscopy Image Classification”, IEEE Transactions on Circuits and Systems for Video Technology, Volume 33, Issue 9, September 2023, Pages 4452 – 4461 [27] Olfat M. Mirza, Aisha Alsobhi, Tawfiq Hasanin, Mohamad Khairi Ishak, Faten Khalid Karim and Samih M. Mostafa, “Computer Aided Diagnosis for Gastrointestinal Cancer Classification using Hybrid Rice Optimization with Deep Learning”, IEEE Access, Volume 11, July 2023, Pages 76321 – 76329 [28] Abdullah Sener and Burhan Ergen, “Automatic detection of gastrointestinal system abnormalities using deep learning-based segmentation and classification methods”, Health Information Science and Systems, Springer, Volume 13, Issue 37, 2025, Pages 1-16 [29] Ayse Ayyuce Demirbas,Huseyin Uzen and Huseyin Fırat,“Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset”,Volume 12, Issue 32, 2024, Page 1-15 [30] Muhammad Attique Khan, Usama Shafiq, Ameer Hamza, Anwar M. Mirza, Jamel Baili, Dina Abdulaziz AlHammadi, Hee-Chan Cho and Byoungchol Chang, “A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images”, BMC Medical Informatics and Decision Making, Volume 25, Issue 150, 2025, Pages 1-15 |
