Abstract: Chest Disease creates serious health issues for human beings all over the world. Identifying these diseases in earlier stages helps people to treat them early and save their life. Conventional Neural Networks play an important role in the health sector especially in predicting diseases in the earlier stages. X-rays are one of the major parameters which help to identify Chest diseases accurately. In this paper, we study the prediction of chest diseases such as Pneumonia, COVID-19, and Tuberculosis (TB) from the X-ray images. The prediction of these diseases is analyzed with the support of three CNN Models such as VGG19, Resnet50V2, and Densenet201, and results are elaborated in the terms of Accuracy and Loss. Though all three models are highly accurate and consistent, considering the factors like architectural size, training speed, etc. Resnet50V2 is the best model for all three diseases. It trained with F1 score accuracies of 0.98,0.92,0.97 for pneumonia, tuberculosis, covid respectively.

Latheesh Mangeri, Gnana Prakasi O S, Neeraj Puppala and Kanmani P, “Chest Diseases Prediction from X-ray Images using CNN Models: A Study” International Journal of Advanced Computer Science and Applications(IJACSA), 12(10), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121026

@article{Mangeri2021,
title = {Chest Diseases Prediction from X-ray Images using CNN Models: A Study},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121026},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121026},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {10},
author = {Latheesh Mangeri and Gnana Prakasi O S and Neeraj Puppala and Kanmani P}
}