A Survey on the Approaches to Detect Pulmonary Fibrosis
Keywords:Machine Learning, Deep Learning, CNN, vanilla quantile regression, image augmentation, SVR, efficientNet-b3, resnet, adam optimizer
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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Copyright (c) 2022 Pranav Pradeep, H. S. Mansi, Likitha Keerthi, Dev Narayanan, K. Sumithra Devi
This work is licensed under a Creative Commons Attribution 4.0 International License.