{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:52:42Z","timestamp":1780501962926,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2022R1G1A1003531"],"award-info":[{"award-number":["2022R1G1A1003531"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["IITP-2022-2020-0-101741"],"award-info":[{"award-number":["IITP-2022-2020-0-101741"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["RS-2022-00155885"],"award-info":[{"award-number":["RS-2022-00155885"]}]},{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP)","award":["2022R1G1A1003531"],"award-info":[{"award-number":["2022R1G1A1003531"]}]},{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP)","award":["IITP-2022-2020-0-101741"],"award-info":[{"award-number":["IITP-2022-2020-0-101741"]}]},{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP)","award":["RS-2022-00155885"],"award-info":[{"award-number":["RS-2022-00155885"]}]},{"name":"Korea government (MSIT)","award":["2022R1G1A1003531"],"award-info":[{"award-number":["2022R1G1A1003531"]}]},{"name":"Korea government (MSIT)","award":["IITP-2022-2020-0-101741"],"award-info":[{"award-number":["IITP-2022-2020-0-101741"]}]},{"name":"Korea government (MSIT)","award":["RS-2022-00155885"],"award-info":[{"award-number":["RS-2022-00155885"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.<\/jats:p>","DOI":"10.3390\/s23031471","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T02:28:34Z","timestamp":1675045714000},"page":"1471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Mudasir","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Bacha Khan University, Charsadda (BKUC), Charsadda 24420, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pir Masoom","family":"Shah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Bacha Khan University, Charsadda (BKUC), Charsadda 24420, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8172-354X","authenticated-orcid":false,"given":"Izaz Ahmad","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Bacha Khan University, Charsadda (BKUC), Charsadda 24420, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9546-4195","authenticated-orcid":false,"given":"Saif ul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Institute of Space Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5416-5802","authenticated-orcid":false,"given":"Zahoor","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), H12, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6220-0225","authenticated-orcid":false,"given":"Faheem","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6393-2994","authenticated-orcid":false,"given":"Youngmoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Robotics, Hanyang University, Ansan-si 15588, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Khan, F., Tarimer, I., and Taekeun, W. 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