Pneumonia Detection Using Novel Deep Learning Techniques

Abstract

Pneumonia is one of the leading infectious disease that can kill children and elderly people around the world. The development of an automated system to detect pneumonia would be advantageous, especially to enable treatment of this disease in remote areas without much delay. pneumonia  is an lung infectious disease which mainly affects the small air sacs known as alveoli. The main symptoms of pneumonia includes cough, fever and breathing problems . Aged people, children and persons who have medical problems are the main victims of this disease. Around 450 million people are affected by this disease on an average of each and every year. The most commonly used technique  for diagnosing this disease  is chest X-ray imaging. Chest X-ray examination is complex procedure to detect the disease because it involves lots of vulnerabilities. With latest advances in technologies we can use deep learning algorithms to detect the disease using chest x ray images. To deal with the scarcity of  data, we used Deep Transfer Learning and designed the Hybrid Algorithms. The images of the chest X-rays were fed into the individual algorithms for training purposes. Parallel Deep Feature Extractors are used in conjunction with various algorithms. For classifying chest X-ray images into normal and pneumonia, Here we are proposing an hybrid model  based on VGG16, VGG19, CNN, and Mobile Net networks. Individual image classification algorithms were combined to form a hybrid model .In comparison to individual algorithms, the new Hybrid Model with Deep Learning achieved higher accuracy than existing methods.