ArticleSensors (Basel, Switzerland)2023
IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography.Frontiers in radiology · 2024Pooled it
- Spine-anchored active contour-based renal spatial prioritization for precision-constrained volumetric segmentation of bilateral nephric tumors via spine-guided 3D deep encoders in abdominopelvic tomographic data.Radiological physics and technology · 2026Article
- Artificial intelligence in computed tomography imaging for pulmonary embolism: a narrative review from computed tomography pulmonary angiography to non-contrast computed tomography.Quantitative imaging in medicine and surgery · 2026Review
- A 19-layer convolutional neural network for accurate COVID-19 detection in chest X-ray images: comparative analysis with pretrained networks.Scientific reports · 2026Article
- White blood cell classification using custom deep neural network and visualizing features of the images using heatmaps.Scientific reports · 2026Article
- Deep learning-based identification of chronic pulmonary embolism on CTPA: a regional lung analysis using multiplanar MIP images.European radiology experimental · 2026Article
- An efficient dual path deep learning framework for COVID-19 classification using lung CT scans with explainable AI.Scientific reports · 2026Article
- LungGANDetectAI: a GAN-augmented and attention-guided deep learning framework for accurate and explainable lung cancer detection.Scientific reports · 2026Article
- Optimized CNN framework for malaria detection using Otsu thresholding-based image segmentation.Scientific reports · 2025Article
- From Innovation to Integration: Bridging the Gap Between IoMT Technologies and Real-World Health Management Systems.Sensors (Basel, Switzerland) · 2025Article
- Improved pulmonary embolism detection in CT pulmonary angiogram scans with hybrid vision transformers and deep learning techniques.Scientific reports · 2025Article
- Comparing efficiency of an attention-based deep learning network with contemporary radiological workflow for pulmonary embolism detection on CTPA: A retrospective study.European journal of radiology open · 2025Article
- A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI.Scientific reports · 2025Article
- Advancements in Machine Learning and Artificial Intelligence in the Radiological Detection of Pulmonary Embolism.Cureus · 2025Review
- Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.Scientific reports · 2024Article
- CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.Sensors (Basel, Switzerland) · 2024Article
- A comprehensive survey on exploring and analyzing COVID-19 mobile apps: Meta and exploratory analysis.Heliyon · 2024Article
- Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.Bioengineering (Basel, Switzerland) · 2024Article
- An Enhanced Mask R-CNN Approach for Pulmonary Embolism Detection and Segmentation.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.