Attention Based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms

Suman, Sudhir; Singh, Gaurav; Sakla, Nicole; Gattu, Rishabh; Green, Jeremy; Phatak, Tej; Samaras, Dimitris; Prasanna, Prateek

doi:10.1007/978-3-030-87234-2_34

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…For the prediction of PE, a two-stage attention-based CNN—long short-term memory (CNN-LSTM) network was developed by Sudhir Suman et al. [ 27 ]. A CNN and the LSTM+Dense sequence model make up the two-stage attention-based model.…”

Section: Related Workmentioning

confidence: 99%

IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning

Khan

Shah

Khan

et al. 2023

Sensors

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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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Section: Related Workmentioning

confidence: 99%

IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning

Khan

Shah

Khan

et al. 2023

Sensors

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“…99,100 The accessibility to PECAD-relevant data, together with the increase in computing power, have sparked a rapid development of PE detection solutions. [101][102][103][104][105][106][107][108][109][110][111][112] Methods for identifying PE rely on conventional image-processing approaches, using segmentation and thresholding techniques, [106][107][108][109] or on DL approaches, [101][102][103][104][105]110,[112][113][114] which mostly rely on CNN architectures. Recently, vision transformers-based architectures 115 have been adopted for PECAD.…”

Section: Pulmonary Embolismmentioning

confidence: 99%

Applications of Artificial Intelligence in Cardiovascular Emergencies – Status Quo and Outlook

Hatfaludi,

Danu,

Leonte

et al. 2023

Journal of Cardiovascular Emergencies

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Cardiovascular diseases are the leading cause of death, with many lives being affected by critical emergencies like heart attacks, strokes, and other acute conditions. Recognizing the early warning signs is crucial for highlighting the need for immediate medical attention, especially since a quick intervention may significantly improve short and long-term patient outcome. Artificial intelligence (AI) has become a key technology in healthcare, and especially in the cardiovascular field. AI, and in particular deep learning is well suited for automatically analyzing medical images, signals, and data. Its success rests on the availability of large amounts of curated data, and the access to high performance computing infrastructures for training the deep-learning algorithms. Thus, in cardiovascular care, AI plays a dynamic role in disease detection, predicting disease outcome, and guiding treatment decisions. This review paper details and discusses the current role of AI for the most common cardiovascular emergencies. It provides insight into the specific issues, risk factors, different subtypes of the diseases, and algorithms developed to date, followed by an outlook.

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PECon: Contrastive Pretraining to Enhance Feature Alignment Between CT and EHR Data for Improved Pulmonary Embolism Diagnosis

Sanjeev,

Al Khatib,

Shaaban

et al. 2023

Lecture Notes in Computer Science

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Attention Based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms

Cited by 10 publications

References 17 publications

IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning

IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning

Applications of Artificial Intelligence in Cardiovascular Emergencies – Status Quo and Outlook

PECon: Contrastive Pretraining to Enhance Feature Alignment Between CT and EHR Data for Improved Pulmonary Embolism Diagnosis

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