Pneumonia Detection from Chest X-ray Images Using Transfer Learning by Fusing the Features of Pre-trained Xception and VGG16 Networks

Abstract

Pneumonia is said to be the "Silent Killer" disease caused by the infection of virus, bacteria, or fungi in the lung alveoli. It bears an extensive risk for people, especially children in some developing nations. The ecumenic way to detect pneumonia is from Chest X-ray data. But it has some complications to diagnose pneumonia if the lung has gone through some surgery, bleeding, the superabundance of fluids, or lung cancer. So, it is necessary to take the help of Computer-Aided Diagnosis (CAD) which can collaborate the doctors to detect pneumonia. Many deep learning methods are applicable to detect pneumonia. Our research introduces a new model generated from the fusion of two different transfer learning models, the Xception model and the VGG16 model. Our research includes image pre-processing using image normalization and augmentation. We took two different transfer learning models namely Xception, and VGG16 for the feature extraction, then added some layers, made a fusion, and lastly added some extra dense layers to develop the proposed model. We took 5216 images of two classes named ‘NORMAL’ and ‘PNEUMONIA’ images to train our model. We took 5216 images to train the model in ‘NORMAL’ and ‘PNEUMONIA’ form. The results were tested with 624 images belonging to two classes. The proposed model achieved accuracy, precision, recall, and f1-score of 91.67%, 92.30%, 89.92%, and 90.87% respectively. The extensive experimental analysis demonstrates the viability of the proposed approach for various test samples.