Automated Detection of Pulmonary Pathologies through Deep Learning in Lung Ultrasound


  • Anjelin Genifer Edward Thomas, J. Shiny Duela


Ultrasound Images, Pulmonary Disease, Deep Learning, Segmentation, Pre-processing, Benchmark Data, Accuracy


The current surge in newly reported pulmonary diseases and the possibility of further epidemics necessitate the immediate development of a novel Deep Learning (DL) model to facilitate early diagnosis and treatment. Lung ultrasound (LUS) has the potential to detect symptoms of a pulmonary infection, based on growing evidence from various nations. Several characteristics of ultrasonic imaging make it well-suited for routine use: Small hand-held systems, unlike X-ray or computed tomography (CT) equipment, are easier to clean because they are encased in a protective sheath. LUS, on the other hand, enables patient triage in settings other than hospitals, such as tents or homes, and it can detect lung activity during the early stages of the disease while also monitoring affected patients at the bedside on a daily basis. This review paper discusses the potential applications of LUS imaging for disease segmentation and categorization. The paper investigates the open-access LUS dataset and examines image processing algorithms that could increase pulmonary disease detection and segmentation accuracy. We also discuss the many segmentation strategies available for LUS images. Next, we present the currently available DL approaches for LUS image categorization. This survey can be extremely beneficial to researchers struggling with disease diagnosis using LUS images, providing excellent advice on how to proceed with their investigation and determine the source of the problem.


Download data is not yet available.


Lang, Hartmut. "Anatomy and Physiology of Respiration." In Out-of Hospital Ventilation: An Interdisciplinary Perspective on Landscape and Health, pp. 3-33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023.

Mira-Avendano, Isabel, Andy Abril, Charles D. Burger, Paul F. Dellaripa, Aryeh Fischer, Michael B. Gotway, Augustine S. Lee et al. "Interstitial lung disease and other pulmonary manifestations in connective tissue diseases." In Mayo Clinic Proceedings, vol. 94, no. 2, pp. 309-325. Elsevier, 2019.

Rosati, Louis A., and Kevin O. Leslie. "Lung infections." Practical pulmonary pathology: a diagnostic approach (2011): 137.

Nishida, Chinatsu, and Kazuhiro Yatera. "The impact of ambient environmental and occupational pollution on respiratory diseases." International Journal of Environmental Research and Public Health 19, no. 5 (2022): 2788.

Malik, Bilal, Basel Abdelazeem, and Abhijeet Ghatol. "Pulmonary fibrosis after COVID-19 pneumonia." Cureus 13, no. 3 (2021).

International Vaccine Access Center Johns Hopkins Bloomberg School of Public Health, Pneumonia and Diarrhea Progress Report 2020, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA, 2020.

Cruz, Alvaro A. Global surveillance, prevention and control of chronic respiratory diseases: a comprehensive approach. World Health Organization, 2007.

Cheng, Elvin S., Marianne Weber, Julia Steinberg, and Xue Qin Yu. "Lung cancer risk in never-smokers: An overview of environmental and genetic factors." Chinese Journal of Cancer Research 33, no. 5 (2021): 548.

Sharma, Shrikamal. "Assessment of Availability and Achievements of the Public Health Care Services in Rural India." Annals of the National Association of Geographers, India 41, no. 1 (2021).

Allwood, B. W., J. Goldin, Q. Said-Hartley, R. N. van Zyl-Smit, G. Calligaro, A. Esmail, N. Beyers, and E. D. Bateman. "Assessment of previous tuberculosis status using questionnaires, chest X-rays and computed tomography scans." The International Journal of Tuberculosis and Lung Disease 19, no. 12 (2015): 1435-1440.

Rehani, Madan M., and David Nacouzi. "Higher patient doses through X-ray imaging procedures." Physica Medica 79 (2020): 80-86.

Tsalafoutas, Ioannis A., Mohammad Hassan Kharita, Huda Al-Naemi, and Mannudeep K. Kalra. "Radiation dose monitoring in computed tomography: Status, options and limitations." Physica Medica 79 (2020): 1-15.

Avola, Danilo, Luigi Cinque, Alessio Fagioli, Gianluca Foresti, and Alessio Mecca. "Ultrasound medical imaging techniques: a survey." ACM Computing Surveys (CSUR) 54, no. 3 (2021): 1-38.

Cootney, Robert W. "Ultrasound imaging: principles and applications in rodent research." Ilar Journal 42, no. 3 (2001): 233-247.

L. Gargani and G. Volpicelli, “How I do it: lung ultrasound,” Cardiovascular Ultrasound, vol. 12, no. 1, pp. 1–10, 2014.

Zhao, Lingyi, and Muyinatu A. Lediju Bell. "A review of deep learning applications in lung ultrasound imaging of COVID-19 patients." BME frontiers 2022 (2022).

Born, Jannis, Gabriel Brändle, Manuel Cossio, Marion Disdier, Julie Goulet, Jérémie Roulin, and Nina Wiedemann. "POCOVID-Net: automatic detection of COVID-19 from a new lung ultrasound imaging dataset (POCUS)." arXiv preprint arXiv:2004.12084 (2020).

Born, Jannis, Nina Wiedemann, Gabriel Brändle, Charlotte Buhre, Bastian Rieck, and Karsten Borgwardt. "Accelerating covid-19 differential diagnosis with explainable ultrasound image analysis." arXiv preprint arXiv:2009.06116 (2020).

Born, Jannis, Nina Wiedemann, Manuel Cossio, Charlotte Buhre, Gabriel Brändle, Konstantin Leidermann, Julie Goulet et al. "Accelerating detection of lung pathologies with explainable ultrasound image analysis." Applied Sciences 11, no. 2 (2021): 672.

Soldati, Gino, Andrea Smargiassi, Riccardo Inchingolo, Danilo Buonsenso, Tiziano Perrone, Domenica Federica Briganti, Stefano Perlini et al. "Proposal for international standardization of the use of lung ultrasound for patients with COVID‐19: a simple, quantitative, reproducible method." Journal of Ultrasound in Medicine 39, no. 7 (2020): 1413-1419.

Roy, Subhankar, Willi Menapace, Sebastiaan Oei, Ben Luijten, Enrico Fini, Cristiano Saltori, Iris Huijben et al. "Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound." IEEE transactions on medical imaging 39, no. 8 (2020): 2676-2687.

Ebadi, Ashkan, Pengcheng Xi, Alexander MacLean, Stéphane Tremblay, Sonny Kohli, and Alexander Wong. "COVIDx-US--An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics." arXiv preprint arXiv:2103.10003 (2021).

Yap, Moi Hoon, Gerard Pons, Joan Marti, Sergi Ganau, Melcior Sentis, Reyer Zwiggelaar, Adrian K. Davison, and Robert Marti. "Automated breast ultrasound lesions detection using convolutional neural networks." IEEE journal of biomedical and health informatics 22, no. 4 (2017): 1218-1226.

Al-Dhabyani, Walid, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy. "Dataset of breast ultrasound images." Data in brief 28 (2020): 104863.

Piotrzkowska‐Wróblewska, Hanna, Katarzyna Dobruch‐Sobczak, Michał Byra, and Andrzej Nowicki. "Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions." Medical physics 44, no. 11 (2017): 6105-6109.

Rodtook, Annupan, Khwunta Kirimasthong, Wanrudee Lohitvisate, and Stanislav S. Makhanov. "Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities." Pattern Recognition 79 (2018): 172-182.

Rukundo, Olivier. "Effects of image size on deep learning." Electronics 12, no. 4 (2023): 985.

Peitgen, Heinz-Otto, Hartmut Jürgens, Dietmar Saupe, Heinz-Otto Peitgen, Hartmut Jürgens, and Dietmar Saupe. "Encoding images by simple transformations." Chaos and Fractals: New Frontiers of Science (1992): 229-296.

Chandel, Ruchika, and Gaurav Gupta. "Image filtering algorithms and techniques: A review." International Journal of Advanced Research in Computer Science and Software Engineering 3, no. 10 (2013).

Pei, Soo-Chang, and Chao-Nan Lin. "Image normalization for pattern recognition." Image and Vision computing 13, no. 10 (1995): 711-723.

Khosla, Cherry, and Baljit Singh Saini. "Enhancing performance of deep learning models with different data augmentation techniques: A survey." In 2020 International Conference on Intelligent Engineering and Management (ICIEM), pp. 79-85. IEEE, 2020.

Singh, Krishna Kant, and Akansha Singh. "A study of image segmentation algorithms for different types of images." International Journal of Computer Science Issues (IJCSI) 7, no. 5 (2010): 414.

Kumar, Rajiv, M. Arthanari, and M. Sivakumar. "Image segmentation using discontinuity-based approach." Int. J. Multimedia Image Process 1 (2011): 72-78.

Ghandorh, Hamza, Wadii Boulila, Sharjeel Masood, Anis Koubaa, Fawad Ahmed, and Jawad Ahmad. "Semantic segmentation and edge detection—Approach to road detection in very high resolution satellite images." Remote Sensing 14, no. 3 (2022): 613.

Bhangale, Harshwardhan, Raghav Bansal, Shrijeet Jain, and Jignesh Sarvaiya. "Multi-feature similarity based deep learning framework for semantic segmentation." In 2021 International Conference on Control, Automation, Power and Signal Processing (CAPS), pp. 1-4. IEEE, 2021.

Al-Amri, Salem Saleh, and Namdeo V. Kalyankar. "Image segmentation by using threshold techniques." arXiv preprint arXiv:1005.4020 (2010).

Blaschke, Thomas, Charles Burnett, and Anssi Pekkarinen. "Image segmentation methods for object-based analysis and classification." In Remote sensing image analysis: Including the spatial domain, pp. 211-236. Dordrecht: Springer Netherlands, 2004.

Kornilov, Anton, Ilia Safonov, and Ivan Yakimchuk. "A review of watershed implementations for segmentation of volumetric images." Journal of Imaging 8, no. 5 (2022): 127.

Menet, Sylvie, Philippe Saint-Marc, and Gerard Medioni. "Active contour models: Overview, implementation and applications." In 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, pp. 194-199. IEEE, 1990.

Delingette, Hervé, and Johan Montagnat. "Shape and topology constraints on parametric active contours." Computer vision and image understanding 83, no. 2 (2001): 140-171.

Reddy, Chandan K., and Bhanukiran Vinzamuri. "A survey of partitional and hierarchical clustering algorithms." In Data clustering, pp. 87-110. Chapman and Hall/CRC, 2018.

Sultana, Farhana, Abu Sufian, and Paramartha Dutta. "Evolution of image segmentation using deep convolutional neural network: A survey." Knowledge-Based Systems 201 (2020): 106062.

Minaee, Shervin, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos. "Image segmentation using deep learning: A survey." IEEE transactions on pattern analysis and machine intelligence 44, no. 7 (2021): 3523-3542.

Ray, Abhisek, and Maheshkumar H. Kolekar. "Image Segmentation and Classification Using Deep Learning." Machine Learning Algorithms for Signal and Image Processing (2022): 19-36.

Alom, Md Zahangir. "Improved deep convolutional neural networks (dcnn) approaches for computer vision and bio-medical imaging." PhD diss., University of Dayton, 2018.

Hossain, Md Anwar, and Md Shahriar Alam Sajib. "Classification of image using convolutional neural network (CNN)." Global Journal of Computer Science and Technology 19, no. 2 (2019).

Hewamalage, Hansika, Christoph Bergmeir, and Kasun Bandara. "Recurrent neural networks for time series forecasting: Current status and future directions." International Journal of Forecasting 37, no. 1 (2021): 388-427.

Dhruv, Patel, and Subham Naskar. "Image classification using convolutional neural network (CNN) and recurrent neural network (RNN): A review." Machine Learning and Information Processing: Proceedings of ICMLIP 2019 (2020): 367-381.

Luo, Wei, Jun Li, Jian Yang, Wei Xu, and Jian Zhang. "Convolutional sparse autoencoders for image classification." IEEE transactions on neural networks and learning systems 29, no. 7 (2017): 3289-3294.

Shen, Yi-Cheng, Te-Chun Hsia, and Ching-Hsien Hsu. "Software Optimization in Ultrasound Imaging Technique Using Improved Deep Belief Learning Network on the Internet of Medical Things Platform." Wireless Personal Communications 127, no. 3 (2022): 2063-2081.

Little, Scott. "Boltzmann Machines and AdS-CFT Stochastic Feynman-Kac Mellin Transform."

Salakhutdinov, Ruslan, and Geoffrey Hinton. "Deep boltzmann machines." In Artificial intelligence and statistics, pp. 448-455. PMLR, 2009.

Huang, Guang-Bin, Qin-Yu Zhu, and Chee-Kheong Siew. "Extreme learning machine: theory and applications." Neurocomputing 70, no. 1-3 (2006): 489-501.

Huang, Gao, Shiji Song, Jatinder ND Gupta, and Cheng Wu. "Semi-supervised and unsupervised extreme learning machines." IEEE transactions on cybernetics 44, no. 12 (2014): 2405-2417.

Alrebdi, Norah, Sarah Alrumiah, Atheer Almansour, and Murad Rassam. "Reinforcement Learning in Image Classification: A Review." In 2022 2nd International Conference on Computing and Information Technology (ICCIT), pp. 79-86. IEEE, 2022.

Diaz-Escobar, Julia, Nelson E. Ordonez-Guillen, Salvador Villarreal-Reyes, Alejandro Galaviz-Mosqueda, Vitaly Kober, Raúl Rivera-Rodriguez, and Jose E. Lozano Rizk. "Deep-learning based detection of COVID-19 using lung ultrasound imagery." Plos one 16, no. 8 (2021): e0255886.

La Salvia, Marco, Gianmarco Secco, Emanuele Torti, Giordana Florimbi, Luca Guido, Paolo Lago, Francesco Salinaro, Stefano Perlini, and Francesco Leporati. "Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification." Computers in biology and medicine 136 (2021): 104742.

Cheng, Dorothy, and Edmund Y. Lam. "Transfer learning U-Net deep learning for lung ultrasound segmentation." arXiv preprint arXiv:2110.02196 (2021).

Gare, Gautam Rajendrakumar, Andrew Schoenling, Vipin Philip, Hai V. Tran, P. deBoisblanc Bennett, Ricardo Luis Rodriguez, and John Michael Galeotti. "Dense pixel-labeling for reverse-transfer and diagnostic learning on lung ultrasound for COVID-19 and pneumonia”




How to Cite

J. Shiny Duela, A. G. E. T. (2024). Automated Detection of Pulmonary Pathologies through Deep Learning in Lung Ultrasound. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1351–1363. Retrieved from



Research Article