Medical Symptoms are sometimes very tricky to analyse in real time as it takes time for example, first to detect the symptoms then to perform some tests and finally coming to a solution. This process can be eliminated and lot of time can be saved by introducing the concept of Deep learning. CNNs create a network for extracting the features of a given image in order to evaluate the image based on the conditions required. This property of the CNN is used as a certain advantage in order to detect the symptoms based on the type of X-ray images provided.

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[1] Anubhav Saha, Yateendra Mishra, "chest-xray-pneumonia", IEEE Dataport, 2021. [Online]. Available: http://dx.doi.org/10.21227/3zek-xv94. Accessed: Jan. 14, 2025.
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doi = {10.21227/3zek-xv94},
url = {http://dx.doi.org/10.21227/3zek-xv94},
author = {Anubhav Saha; Yateendra Mishra },
publisher = {IEEE Dataport},
title = {chest-xray-pneumonia},
year = {2021} }
TY - DATA
T1 - chest-xray-pneumonia
AU - Anubhav Saha; Yateendra Mishra
PY - 2021
PB - IEEE Dataport
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Anubhav Saha, Yateendra Mishra. (2021). chest-xray-pneumonia. IEEE Dataport. http://dx.doi.org/10.21227/3zek-xv94
Anubhav Saha, Yateendra Mishra, 2021. chest-xray-pneumonia. Available at: http://dx.doi.org/10.21227/3zek-xv94.
Anubhav Saha, Yateendra Mishra. (2021). "chest-xray-pneumonia." Web.
1. Anubhav Saha, Yateendra Mishra. chest-xray-pneumonia [Internet]. IEEE Dataport; 2021. Available from : http://dx.doi.org/10.21227/3zek-xv94
Anubhav Saha, Yateendra Mishra. "chest-xray-pneumonia." doi: 10.21227/3zek-xv94