{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T17:03:26Z","timestamp":1779901406900,"version":"3.53.1"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Najran University","award":["NU\/RG\/SERC\/11\/4"],"award-info":[{"award-number":["NU\/RG\/SERC\/11\/4"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor\u2019s judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models.<\/jats:p>","DOI":"10.3390\/s23062932","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T02:01:47Z","timestamp":1678327307000},"page":"2932","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer"],"prefix":"10.3390","volume":"23","author":[{"given":"Reyazur Rashid","family":"Irshad","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6147-403X","authenticated-orcid":false,"given":"Shahid","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 30019, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5944-7371","authenticated-orcid":false,"given":"Shahab Saquib","family":"Sohail","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0135-2337","authenticated-orcid":false,"given":"Abu Sarwar","family":"Zamani","sequence":"additional","affiliation":[{"name":"Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8735-3332","authenticated-orcid":false,"given":"Dag \u00d8ivind","family":"Madsen","sequence":"additional","affiliation":[{"name":"USN School of Business, University of South-Eastern Norway, 3511 H\u00f8nefoss, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8463-7428","authenticated-orcid":false,"given":"Ahmed Abdu","family":"Alattab","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia"},{"name":"Department of Computer Science, Faculty of Computer Science and Information Systems, Thamar University, Thamar 87246, Yemen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdallah Ahmed Alzupair","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalid Ahmed Abdallah","family":"Norain","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar Ali Saleh","family":"Alsaiari","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,8]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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