IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning
DC Field | Value | Language |
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dc.contributor.author | Khan, Mudasir | - |
dc.contributor.author | Shah, Pir Masoom | - |
dc.contributor.author | Khan, Izaz Ahmad | - |
dc.contributor.author | ul Islam, Saif | - |
dc.contributor.author | Ahmad, Zahoor | - |
dc.contributor.author | Khan, Faheem | - |
dc.contributor.author | Lee, Youngmoon | - |
dc.date.accessioned | 2023-04-03T10:02:25Z | - |
dc.date.available | 2023-04-03T10:02:25Z | - |
dc.date.issued | 2023-02 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/111630 | - |
dc.description.abstract | 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. | - |
dc.format.extent | 18 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | - |
dc.title | IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/s23031471 | - |
dc.identifier.scopusid | 2-s2.0-85147894700 | - |
dc.identifier.wosid | 000929544800001 | - |
dc.identifier.bibliographicCitation | Sensors, v.23, no.3, pp 1 - 18 | - |
dc.citation.title | Sensors | - |
dc.citation.volume | 23 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 18 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | CONVOLUTIONAL NEURAL-NETWORK | - |
dc.subject.keywordAuthor | pulmonary embolism | - |
dc.subject.keywordAuthor | computed tomography scans | - |
dc.subject.keywordAuthor | computer-aided diagnosis (CAD) | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | CNN | - |
dc.subject.keywordAuthor | DenseNet201 | - |
dc.identifier.url | https://www.mdpi.com/1424-8220/23/3/1471 | - |
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