A Survey on the Approaches to Detect Pulmonary Fibrosis

Authors

  • Pranav Pradeep Student, Department of Information Science and Engineering, Dayanand Sagar Academy of Technology and Management, Bengaluru, India
  • H. S. Mansi Student, Department of Information Science and Engineering, Dayanand Sagar Academy of Technology and Management, Bengaluru, India
  • Likitha Keerthi Student, Department of Information Science and Engineering, Dayanand Sagar Academy of Technology and Management, Bengaluru, India
  • Dev Narayanan Student, Department of Information Science and Engineering, Dayanand Sagar Academy of Technology and Management, Bengaluru, India
  • K. Sumithra Devi Professor & HoD, Department of Information Science and Engineering, Dayanand Sagar Academy of Technology and Management, Bengaluru, India

Keywords:

Machine Learning, Deep Learning, CNN, vanilla quantile regression, image augmentation, SVR, efficientNet-b3, resnet, adam optimizer

Abstract

Pulmonary fibrosis is an incurable, fatal, and debilitating disease that damages the patient's respiratory system, making it difficult to live with. Despite the fact that the situation appears to be hopeless, modern medicine can help to postpone the disease's prognosis. The ability of the doctor to determine the severity of the sickness becomes critical for appropriate therapy, yet this is a highly risky decision. We suggest a unique way to address this bottleneck problem by constructing a system that can accurately anticipate disease progression for a given week by measuring the patient's FVC value. This saves the pulmonologist time and effort while potentially extending a person's life. The suggested approach predicts FVC output for a given week by combining image and tabular data.

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Published

22-04-2022

How to Cite

[1]
P. Pradeep, H. S. Mansi, L. Keerthi, D. Narayanan, and K. S. Devi, “A Survey on the Approaches to Detect Pulmonary Fibrosis”, IJRESM, vol. 5, no. 4, pp. 96–99, Apr. 2022.

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