{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:06:30Z","timestamp":1758931590552,"version":"3.44.0"},"reference-count":31,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Gastrointestinal (GI) disorders represent a significant challenge in healthcare, underscoring the necessity for more precise and effective diagnostic techniques. Conventional approaches, which often rely on single models, have demonstrated shortcomings in both accuracy and efficacy, often failing to detect the intricate and varied patterns linked to these diseases. To overcome these challenges, this study introduces a novel ensemble learning framework tailored for GI detection. The framework utilizes a three\u2010layer architectural approach that integrates Convolutional Neural Networks (CNNs), the Ant Colony Optimization Algorithm (ACO), and Weighted Aggregation Ensemble Techniques (WAET). The methodology unfolds in three key stages: First, multiple CNNs are fine\u2010tuned using transfer learning, while ACO optimizes the hyperparameters of each CNN to enhance model adaptability and performance. Second, the predictions from the top three optimized models are combined using WAET to strengthen the system's robustness in GI detection. Lastly, ACO is employed to optimize the weight assignment for each model during the ensembling process. We use a dataset of 6000 endoscopy images, enhanced by cropping and augmentation techniques to boost diversity and improve classification performance. Additional experiments on CP\u2010Child\u2010A and CP\u2010Child\u2010B show that the proposed ensemble model achieves superior performance, with an accuracy of 99.88% on the primary dataset and 98.75% and 100% on CP\u2010Child\u2010A and B, respectively. It outperforms traditional hybrid methods and state\u2010of\u2010the\u2010art approaches. The effectiveness of the model is further validated through interpretability techniques like Grad\u2010CAM and SHAP, providing insights into the decision\u2010making process. This approach enhances diagnostic accuracy and provides a robust, interpretable solution for automated detection of GI diseases, improving clinical decision\u2010making.<\/jats:p>","DOI":"10.1002\/ima.70214","type":"journal-article","created":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T18:24:58Z","timestamp":1758479098000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ant Colony Optimization\u2010Based Deep Ensemble Learning Model for Improved Gastrointestinal Disease Detection"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0707-470X","authenticated-orcid":false,"given":"Sohaib","family":"Asif","sequence":"first","affiliation":[{"name":"Taizhou Key Laboratory of Minimally Invasive Interventional Therapy &amp; Artificial Intelligence Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital)  Taizhou China"},{"name":"Center of Intelligent Diagnosis and Therapy (Taizhou), Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences  Taizhou China"},{"name":"Wenling Institute of big Data and Artificial Intelligence in Medicine  Taizhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingying","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiology Imaging Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital)  Taizhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenqiu","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Radiology Imaging Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital)  Taizhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongbiao","family":"Ying","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgical Oncology Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital)  Taizhou 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