International Journal of Performance Analysis in Sport, 2021, 17(3): 322-332 doi: 10.23940/ijpe.21.03.p8.322332

Original article

Detecting Pulmonary Embolism using Deep Neural Networks

J. Akilandeswaria,1,2,3, G. Jothib1,2,3, A. Naveenkumara1,2,3, R.S. Sabeenianc1,2,3, P. Iyyanara1,2,3, M.E Paramasivamc1,2,3

Department of IT, Sona College of Technology, Salem, 636005, India

Department of Computer Applications, Sona College of Arts and Science, Salem, 636005, India

Department of ECE, Sona College of Technology, Salem, 636005, India

*Corresponding Author(s): Corresponding author. E-mail address: Corresponding author. E-mail address:


Medical image processing is a method to create visual representations of the internal parts of the human body such as organs or tissues which helps diagnose and monitor diseases. Pulmonary Embolism (PE) is a medical issue where there is an artery obstruction in the lungs. PE is the third most common cause of cardiovascular death and is related to multiple inherited and acquired risk factors. The earlier diagnosis of PE detection helps to increase the patient's survival. With the advancement of Artificial Intelligence (AI), deep learning has become the leading technique, as it established significant capabilities in medical image processing tasks. In this research, a popular deep learning technique called Convolution Neural Network (CNN) is used to detect the pulmonary embolism in lung CT scan images. Four different types of predefined CNN architectures such as the Inception, VGG-16, ResNet50, and Mobilenet are used to compare the performance of the CNN model. In this experiment, RSNA STR Pulmonary Embolism Chest CT scan image dataset is analyzed. The empirical results show that the Inception-based CNN model provides better results when compared to other CNN architectures.

Keywords: Pulmonary Embolism ; deep learning ; convolution neural network ; VGG-16 ; ResNet50 ; Inception ; Mobilenet

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Cite this article

J. Akilandeswaria, G. Jothib, A. Naveenkumara, R.S. Sabeenianc, P. Iyyanara, M.E Paramasivamc. Detecting Pulmonary Embolism using Deep Neural Networks. International Journal of Performance Analysis in Sport, 2021, 17(3): 322-332 doi:10.23940/ijpe.21.03.p8.322332



Biomedical imaging, visualization, and analysis

John Wiley and Sons, Inc., 1999.

BĕlohlávekJ., DytrychV. and LinhartA.

Pulmonary embolism, part I: Epidemiology, risk factors and risk stratification, pathophysiology, clinical presentation, diagnosis and nonthrombotic pulmonary embolism

Experimental and Clinical Cardiology, 18(2), p.129, 2013.

MaX., NiuY., GuL., WangY., ZhaoY., BaileyJ. and LuF.

Understanding adversarial attacks on deep learning based medical image analysis systems

Pattern Recognition, 110, p. 107332, 2021.

YangX., LinY., SuJ., WangX., LiX., LinJ. and ChengK.T.

A two-stage convolutional neural network for pulmonary embolism detection from CTPA images

IEEE Access, 7, pp.84849-84857, 2019.

ShiL, RajanD, AbedinS, YellapragadaM.S, BeymerD, and DehghanE. Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study. In Proceedings of the Third Conference on Medical Imaging with Deep Learning, 121, pp.743-754, 2020.

RajanD., BeymerD., AbedinS. and DehghanE.

Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images

In Machine Learning for Health Workshop, pp.220-232, 2020.

HuangS.C., KothariT., BanerjeeI., ChuteC., BallR.L., BorusN., HuangA., PatelB.N., RajpurkarP., IrvinJ. and DunnmonJ.

PENet—A scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging

NPJ digital medicine, 3(1), pp.1-9, 2020.

LongK., TangL., PuX., RenY., ZhengM., GaoL., SongC., HanS., ZhouM. and DengF.

Probability-based Mask R-CNN for pulmonary embolism detection

Neurocomputing, 422, pp.345-353, 2021.

MuenzelD., FingerleA.A., ZahelT., SauterA., VlassenbroekA., DobritzM., RummenyE.J.and NoëlP.B.

CT angiography: post-processed contrast enhancement for improved detection of pulmonary embolism

Academic radiology, 24(2), pp.131-136, 2017.

LeCunY., BottouL., BengioY. and HaffnerP.

Gradient-based learning applied to document recognition

In Proceedings of the IEEE, 86(11), pp.2278-2324, 1998.

SimonyanK, and ZissermanA. Very deep convolutional networks for large-scale image recognition. ICLR 2015, 2015.

HeK., ZhangX., RenS. and SunJ.

Deep residual learning for image recognition

In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

SzegedyC., LiuW., JiaY., SermanetP., ReedS., AnguelovD., ErhanD., VanhouckeV. and RabinovichA.

Going deeper with convolutions

In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1-9, 2015.

HowardA. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M. and Adam, H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv: 1704. 04861, 2017. (last accessed March 2021)

TajbakhshN., ShinJ.Y., GotwayM.B.and LiangJ.

Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation

Medical image analysis, 58, p. 101541, 2019.