• RITU GILL
• Ms ARCHANA ROHILLA
• Mr.Yash Dhankhar

The platform will enable its users and professional diagnostic centers to upload their chest radiographs (x-rays) and get accurate predictions based on those. Chest radiography has important clinical value in the diagnosis of diseases."Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia.'' However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used the dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases. The automatic detection of chest disease based on chest radiography has become a hot topic in medical imaging research. This project has overall two parts

LUNG DISEASE DETECTION METHODOLOGY, MACHINE LEARNING, ARTIFICIAL INTELLIGENCE.

"LUNG DISEASE DETECTION METHODOLOGY ADOPTED (During Covid-19)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 9, page no.a538-a545, September-2021, Available :http://www.jetir.org/papers/JETIR2109076.pdf

"LUNG DISEASE DETECTION METHODOLOGY ADOPTED (During Covid-19)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 9, page no. ppa538-a545, September-2021, Available at : http://www.jetir.org/papers/JETIR2109076.pdf