{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T20:12:03Z","timestamp":1767211923697,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Liaoning Provincial Department of Education Basic Research Project for Higher Education Institutions","award":["LJKZ0159","JYTMS20231160","JG22DB488","202200209","22-319-2-26"],"award-info":[{"award-number":["LJKZ0159","JYTMS20231160","JG22DB488","202200209","22-319-2-26"]}]},{"name":"Basic Research Project of Liaoning Provincial Department of Education","award":["LJKZ0159","JYTMS20231160","JG22DB488","202200209","22-319-2-26"],"award-info":[{"award-number":["LJKZ0159","JYTMS20231160","JG22DB488","202200209","22-319-2-26"]}]},{"name":"Research on the Construction of a New Artificial Intelligence Technology and High-Quality Education Service Supply System in the 14th Five-Year Plan for Education Science in Liaoning Province, 2023\u20132025","award":["LJKZ0159","JYTMS20231160","JG22DB488","202200209","22-319-2-26"],"award-info":[{"award-number":["LJKZ0159","JYTMS20231160","JG22DB488","202200209","22-319-2-26"]}]},{"name":"\u201cChunhui Plan\u201d of the Ministry of Education, Research on Optimization Model and Algorithm for Microgrid Energy Scheduling Based on Biological Behavior","award":["LJKZ0159","JYTMS20231160","JG22DB488","202200209","22-319-2-26"],"award-info":[{"award-number":["LJKZ0159","JYTMS20231160","JG22DB488","202200209","22-319-2-26"]}]},{"name":"Shenyang Science and Technology Plan \u201cSpecial Mission for Leech Breeding and Traditional Chinese Medicine Planting in Dengshibao Town, Faku County\u201d","award":["LJKZ0159","JYTMS20231160","JG22DB488","202200209","22-319-2-26"],"award-info":[{"award-number":["LJKZ0159","JYTMS20231160","JG22DB488","202200209","22-319-2-26"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Current stomach disease detection and diagnosis is challenged by data complexity and high dimensionality and requires effective deep learning algorithms to improve diagnostic accuracy. To address this challenge, in this paper, an improved strategy based on the Adam algorithm is proposed, which aims to alleviate the influence of local optimal solutions, overfitting, and slow convergence rates by controlling the restart strategy and the gradient norm joint clipping technique. This improved algorithm is abbreviated as the CG-Adam algorithm. The control restart strategy performs a restart operation by periodically checking the number of steps and once the number of steps reaches a preset restart period. After the restart is completed, the algorithm will restart the optimization process. It helps the algorithm avoid falling into the local optimum and maintain convergence stability. Meanwhile, gradient norm joint clipping combines both gradient clipping and norm clipping techniques, which can avoid gradient explosion and gradient vanishing problems and help accelerate the convergence of the optimization process by restricting the gradient and norm to a suitable range. In order to verify the effectiveness of the CG-Adam algorithm, experimental validation is carried out on the MNIST, CIFAR10, and Stomach datasets and compared with the Adam algorithm as well as the current popular optimization algorithms. The experimental results demonstrate that the improved algorithm proposed in this paper achieves an accuracy of 98.59%, 70.7%, and 73.2% on the MNIST, CIFAR10, and Stomach datasets, respectively, surpassing the Adam algorithm. The experimental results not only prove the significant effect of the CG-Adam algorithm in accelerating the model convergence and improving generalization performance but also demonstrate its wide potential and practical application value in the field of medical image recognition.<\/jats:p>","DOI":"10.3390\/a17070272","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T05:33:28Z","timestamp":1718948008000},"page":"272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Improved Adam\u2019s Algorithm for Stomach Image Classification"],"prefix":"10.3390","volume":"17","author":[{"given":"Haijing","family":"Sun","sequence":"first","affiliation":[{"name":"School of Intelligent Science and Engineering, Shenyang University, Shenyang 110044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shenyang University, Shenyang 110044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yichuan","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Engineering, Shenyang University, Shenyang 110044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiantao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shenyang University, Shenyang 110044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Chemistry and Chemical Engineering, University of Surrey, Guildford GU2 7XH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5533-7645","authenticated-orcid":false,"given":"Le","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Engineering, Shenyang University, Shenyang 110044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Science, Shenyang University of Technology, Shenyang 110044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yun, J. 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Eng."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/7\/272\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:02:10Z","timestamp":1760108530000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/7\/272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,21]]},"references-count":20,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["a17070272"],"URL":"https:\/\/doi.org\/10.3390\/a17070272","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2024,6,21]]}}}