Pneumonia primarily affects individuals who are either older than 65 years or younger than five years. Timely identification and prompt treatment of pneumonia can significantly improve the chances of survival for individuals. Pneumonia detection often involves extensive analysis of Chest X-ray images. Recent research indicates that the utilization of deep learning technique holds significant promise in the accurate identification and diagnosis of pneumonia. A novel approach is proposed in this research, where a hybrid Convolutional Neural Network is introduced for the purpose of pneumonia detection in chest X-ray images. In this approach, initially images of Chest X-ray are gathered and preprocessed. Later feature extraction was done using VGG16 and VGG19 model. After training and testing Machine Learning (ML) classifiers, an ensemble classifier was created for classification of pneumonia. Experiment results shows that ensemble classifier outperforms existing state of art methods by exhibiting superior accuracy and recall performance.

Pneumonia,Chest X-ray, VGG16, VGG19, Ensemble Classifier, Convolutional Neural Network

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