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Impact of Chronic Lung Disease Using Deep Learning: A Survey

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Computer, Communication, and Signal Processing (ICCCSP 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 651))

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Abstract

Artificial intelligence has developed in recent years. It is mostly enviable to discover the facility of contemporaneous state-of-the-art techniques and to examine lung nodule features in terms of a large population. Now a days lung plays a major role all over the world in early prevention in disease identification. The latest progress of deep learning sustains the recognition and categorization of medical images of respiratory problems. There are varieties of lung diseases to be analyzed to select the high mortality rate among them. In this paper, we have provided a comprehensive study of several lung ailments, in particular lung cancer, pneumonia, and COVID-19/SARS, Chronic Obstructive Pulmonary Disease. Existing deep learning methodology used to diagnose lung diseases are clearly explained and it will be helpful for the lung disease identify the system.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, India

    N. Vignesh Kumaran & D. M. D. Preethi

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  1. N. Vignesh Kumaran
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  2. D. M. D. Preethi
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Correspondence to N. Vignesh Kumaran .

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Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Erich J. Neuhold

  2. Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  3. University of Huddersfield, Huddersfield, UK

    Joan Lu

  4. University of Florida, Gainesville, FL, USA

    Selwyn Piramuthu

  5. SSN College of Engineering, Kalavakkam, India

    Aravindan Chandrabose

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Kumaran, N.V., Preethi, D.M.D. (2022). Impact of Chronic Lung Disease Using Deep Learning: A Survey. In: Neuhold, E.J., Fernando, X., Lu, J., Piramuthu, S., Chandrabose, A. (eds) Computer, Communication, and Signal Processing. ICCCSP 2022. IFIP Advances in Information and Communication Technology, vol 651. Springer, Cham. https://doi.org/10.1007/978-3-031-11633-9_5

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  • DOI: https://doi.org/10.1007/978-3-031-11633-9_5

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