{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T11:58:56Z","timestamp":1769169536902,"version":"3.49.0"},"reference-count":19,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T00:00:00Z","timestamp":1675468800000},"content-version":"vor","delay-in-days":34,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>\u201cMalignant mesothelioma (MM)\u201d is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is \u201cmalignant mesothelioma.\u201d Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination \u201cepithelial\/mesenchymal appearance of MM,\u201d however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful \u201cMalignant Mesothelioma illness\u201d identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support \u201cVector Machine, Neural Network, and Decision Tree\u201d are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.<\/jats:p>","DOI":"10.1155\/2023\/3164166","type":"journal-article","created":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T18:20:05Z","timestamp":1675534805000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["[Retracted] Applications of Neural Network\u2010Based Plan\u2010Cancer Method for Primary Diagnosis of Mesothelioma Cancer"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8064-6298","authenticated-orcid":false,"given":"Dhiraj","family":"Kapila","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5303-8307","authenticated-orcid":false,"given":"Sarika","family":"Panwar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1158-8386","authenticated-orcid":false,"given":"M. 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The mesothelioma disease diagnosis with artificial intelligence methods 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT) 2016 October Baku Azerbaijan 1\u20135 https:\/\/doi.org\/10.1109\/ICAICT.2016.7991825 2-s2.0-85034265244.","DOI":"10.1109\/ICAICT.2016.7991825"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2018.1451216"},{"key":"e_1_2_9_5_2","article-title":"Artificial intelligence based decision support system for early diagnosis of mesothelioma disease","volume":"2","author":"\u00d6lmez E.","year":"2022","journal-title":"Artificial Intelligence Theory and Applications"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxab146"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.3233\/THC-202237"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxab015"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2017.07.054"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.Matpr.2021.03.642"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.07.288"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.27979"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.5109\/4742117"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.06.272"},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","unstructured":"LeongM. A comparative study on machine learning algorithms and a hybrid model of genetic algorithm and neural network for mesothelioma diagnosis 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) 2020 October Via del Mar Chile 1\u20138 https:\/\/doi.org\/10.1109\/CIBCB48159.2020.9277725.","DOI":"10.1109\/CIBCB48159.2020.9277725"},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","unstructured":"WinK. Y. ManeeratN. ChoomchuayS. SrengS. andHamamotoK. Suitable supervised machine learning techniques for malignant mesothelioma diagnosis 2018 11th Biomedical Engineering International Conference (BMEiCON) 2018 November Chiang Mai Thailand 1\u20135 https:\/\/doi.org\/10.1109\/BMEiCON.2018.8609935 2-s2.0-85062076210.","DOI":"10.1109\/BMEiCON.2018.8609935"},{"key":"e_1_2_9_17_2","first-page":"1604","article-title":"A machine learning based early diagnosis system for mesothelioma disease","volume":"8","author":"\u015eent\u00fcrk Z. 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Classification of malignant mesothelioma cancer using support vector machine 2018 International Conference on Computing Mathematics and Engineering Technologies (iCoMET) 2018 March Sukkur Pakistan 1\u20135 https:\/\/doi.org\/10.1109\/ICOMET.2018.8346411 2-s2.0-85050975140.","DOI":"10.1109\/ICOMET.2018.8346411"}],"updated-by":[{"DOI":"10.1155\/2024\/9861895","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"record-id":"57350"},{"DOI":"10.1155\/2024\/9861895","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000}}],"container-title":["BioMed Research International"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2023\/3164166.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2023\/3164166.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2023\/3164166","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T23:03:25Z","timestamp":1722899005000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2023\/3164166"}},"subtitle":[],"editor":[{"given":"Gaganpreet","family":"Kaur","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":19,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["10.1155\/2023\/3164166"],"URL":"https:\/\/doi.org\/10.1155\/2023\/3164166","archive":["Portico"],"relation":{"retraction":[{"id-type":"doi","id":"10.1155\/2024\/9861895","asserted-by":"object"}]},"ISSN":["2314-6133","2314-6141"],"issn-type":[{"value":"2314-6133","type":"print"},{"value":"2314-6141","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"2022-07-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-11-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"3164166"}}