IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learningopen access
- Authors
- Khan, Mudasir; Shah, Pir Masoom; Khan, Izaz Ahmad; ul Islam, Saif; Ahmad, Zahoor; Khan, Faheem; Lee, Youngmoon
- Issue Date
- Feb-2023
- Publisher
- MDPI
- Keywords
- pulmonary embolism; computed tomography scans; computer-aided diagnosis (CAD); deep learning; CNN; DenseNet201
- Citation
- SENSORS, v.23, no.3
- Journal Title
- SENSORS
- Volume
- 23
- Number
- 3
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87070
- DOI
- 10.3390/s23031471
- ISSN
- 1424-8220
- 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.
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