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Pneumonia is a fatal disease that immensely affects the elderly and may prove to be life threatening. Early diagnosis of Pneumonia gains a primary importance for saving many human lives. This work aims at the detection and classification of patients affected by Pneumonia based on their chest X-rays.Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model. Deep Learning models automate the process and ensure speedy, adroit, and adept results when provided with X-rays of patients. The accuracy should increases as the model trains and decreases the loss simultaneously. Overfitting is may be prevented by implementing data augmentation before fitting the model. This model could help attenuate the reliability and comprehensibility challenges often faced when dealing with medical imagery.
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CC Attribution-NoDerivatives 4.0