Deep Learning and X‐Ray Imaging Innovations for Pneumonia Infection Diagnosis: Introducing DeepPneuNet

ABSTRACT This paper aims to develop a new deep learning model (DeepPneuNet) and evaluate its performance in predicting Pneumonia infection diagnosis based on patients' chest x‐ray images. We have collected 5856 chest x‐ray images that are labeled as either “pneumonia” or “normal” from a public forum. Before applying the DeepPneuNet model, a necessary feature extraction and feature mapping have been done on the input images. Conv2D layers with a 1 × 1 kernel size are followed by ReLU activation functions to make up the model. These layers are in charge of recognizing important patterns and features in the images. A MaxPooling 2D procedure is applied to minimize the spatial size of the feature maps after every two Conv2D layers. The sparse categorical cross‐entropy loss function trains the model, and the Adam optimizer with a learning rate of 0.001 is used to optimize it. The DeepPneuNet provides 90.12% accuracy for diagnosis of the Pneumonia infection for a set of real‐life test images. With 9,445,586 parameters, the DeepPneuNet model exhibits excellent parameter efficiency. DeepPneuNet is a more lightweight and computationally efficient alternative when compared to the other pre‐trained models. We have compared accuracies for predicting Pneumonia diagnosis of our proposed DeepPneuNet model with some state‐of‐the‐art deep learning models. The proposed DeepPneuNet model is more advantageous than the existing state‐of‐the‐art learning models for Pneumonia diagnosis with respect to accuracy, precision, recall, F ‐score, training parameters, and training execution time.