Pre-trained Convolutional Neural Network Model Based Pneumonia Classification from Chest X-Ray Images

Abstract

Pneumonia is a life-threatening infectious lung disease in humans. It causes the air sacs of the lungs to be filled up with fluid or pus. This ranges from mild to severe, based on the type of germ causing the infection. There are various causes for pneumonia. One of the major causes include bacteria like Streptococcus and viruses like influenza, corona virus and so on. The treatment for bacterial pneumonia varies from viral pneumonia. So, identifying the type of pneumonia is essential to provide a better treatment. According to the World Health Organization, pneumonia is responsible for one out of every three deaths in India. The early detection of pneumonia is essential to provide the treatment in the right time. The diagnosis of chest X-rays needs expert radiotherapists for evaluation. Even after the detection, it is a challenge for the doctors to classify pneumonia accurately. The system aims in classifying pneumonia from chest X-ray images thereby helps to save time and to increase accuracy in prediction. Deep Learning algorithms have been effective in analyzing medical images and has gained much attention for disease classification. This paper presents Convolutional Neural Network (CNN) architecture to extract features from the image and classify them using the extracted features. A pre-trained Xception model along with data augmentation techniques is employed to classify the chest x-ray images as bacterial or viral pneumonia with better accuracy. This helps the doctors to provide timely assistance to the patients.