A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks

Abstract

Pneumonia is a viral infection which affects a significant proportion of individuals, especially in developing and penurious countries where contamination, overcrowded, and unsanitary living conditions are widespread, along with the lack of healthcare infrastructures. Pneumonia produces pericardial effusion, a disease wherein fluids fill the chest and create inhaling problems. It is a difficult step to recognize the presence of pneumonia quickly in order to receive treatment services and improve survival chances. Deep learning, is a field of artificial intelligence which is used in the successful development of prediction models. There are various ways of detecting pneumonia such as CT-scan, pulse oximetry, and many more among which the most common way is X-ray tomography. On the other hand, examining chest X-rays (CXR) is a tough process susceptible to subjective variability. In this work, a deep learning(DL) model using VGG16 is utilized for detecting and classifying pneumonia using two CXR image datasets. The VGG16 with Neural Networks (NN) provides an accuracy value of 92.15%, recall as 0.9308, precision as 0.9428, and F1-Score0.937 for the first dataset. Furthermore, the experiment using NN with VGG16 has been performed on another CXR dataset containing 6,436 images of pneumonia, normal and covid-19. The results for the second dataset provide accuracy, recall, precision, and F1-score as 95.4%, 0.954, 0.954, and 0.954, respectively. The research outcome exhibits that VGG16 with NN provides better performance than VGG16 with Support Vector Machine (SVM), VGG16 with K-Nearest Neighbor (KNN), VGG16 with Random Forest (RF), and VGG16 with Naïve Bayes (NB) for both datasets. Further, the proposed work results exhibit improved performance results for both datasets 1 and 2 in comparison to existing models.