{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T05:18:55Z","timestamp":1781327935626,"version":"3.54.1"},"reference-count":75,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["Research Chair of Smart Technologies"],"award-info":[{"award-number":["Research Chair of Smart Technologies"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occurrences. The detection of lung regions in images is a classic medical challenge. Studies show that computational methods contribute significantly to the medical diagnosis of lung pathologies by Computerized Tomography (CT), as well as through Internet of Things (IoT) methods based in the context on the health of things. The present work proposes a new model based on IoT for classification and segmentation of pulmonary CT images, applying the transfer learning technique in deep learning methods combined with Parzen\u2019s probability density. The proposed model uses an Application Programming Interface (API) based on the Internet of Medical Things to classify lung images. The approach was very effective, with results above 98% accuracy for classification in pulmonary images. Then the model proceeds to the lung segmentation stage using the Mask R-CNN network to create a pulmonary map and use fine-tuning to find the pulmonary borders on the CT image. The experiment was a success, the proposed method performed better than other works in the literature, reaching high segmentation metrics values such as accuracy of 98.34%. Besides reaching 5.43 s in segmentation time and overcoming other transfer learning models, our methodology stands out among the others because it is fully automatic. The proposed approach has simplified the segmentation process using transfer learning. It has introduced a faster and more effective method for better-performing lung segmentation, making our model fully automatic and robust.<\/jats:p>","DOI":"10.3390\/s20236711","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T09:06:28Z","timestamp":1606208788000},"page":"6711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation"],"prefix":"10.3390","volume":"20","author":[{"given":"Lu\u00eds Fabr\u00edcio de Freitas","family":"Souza","sequence":"first","affiliation":[{"name":"Department of Computer Science, Federal Institute of Education, Science and Technology of Cear\u00e1, Fortaleza CE 60040-215, Brazil"},{"name":"Department of Teleinformatics Engineering, Federal University of Cear\u00e1, Fortaleza CE 60020-181, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I\u00e1gson Carlos Lima","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal Institute of Education, Science and Technology of Cear\u00e1, Fortaleza CE 60040-215, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriell Gomes","family":"Marques","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal Institute of Education, Science and Technology of Cear\u00e1, Fortaleza CE 60040-215, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco H\u00e9rcules dos S.","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal Institute of Education, Science and Technology of Cear\u00e1, Fortaleza CE 60040-215, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Virg\u00ednia Xavier","family":"Nunes","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal Institute of Education, Science and Technology of Cear\u00e1, Fortaleza CE 60040-215, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3479-3606","authenticated-orcid":false,"given":"Mohammad Mehedi","family":"Hassan","sequence":"additional","affiliation":[{"name":"Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor Hugo C. de","family":"Albuquerque","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal Institute of Education, Science and Technology of Cear\u00e1, Fortaleza CE 60040-215, Brazil"},{"name":"Department of Teleinformatics Engineering, Federal University of Cear\u00e1, Fortaleza CE 60020-181, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1878-5489","authenticated-orcid":false,"given":"Pedro P. Rebou\u00e7as","family":"Filho","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal Institute of Education, Science and Technology of Cear\u00e1, Fortaleza CE 60040-215, Brazil"},{"name":"Department of Teleinformatics Engineering, Federal University of Cear\u00e1, Fortaleza CE 60020-181, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Buzug, T.M. (2011). Computed tomography. Springer Handbook of Medical Technology, Springer.","DOI":"10.1007\/978-3-540-74658-4_16"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gualtieri, P., Falcone, C., Romano, L., Macheda, S., Correale, P., Arciello, P., Polimeni, N., and Lorenzo, A.D. (2020). Body composition findings by computed tomography in SARS-CoV-2 patients: Increased risk of muscle wasting in obesity. Int. J. Mol. 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