Classification of Pneumonia Using Deep Convolutional Neural Network

Abstract

Pneumonia is considered a serious and fatal disease worldwide. In fact, pneumonia can be an individual's life-endangering if not treated promptly by drugs. Therefore, the early detection of pneumonia enhances the chances of recovery, which helps reduce mortality. It is worth noting that X-rays are one of the most important diagnostic tools for diagnosing pneumonia. In fact, Chest X-ray is widely used in the diagnosis of many lung diseases (such as: Breast Cancer, Pneumonia, Tuberculosis, etc.), due to lower diagnostic costs. Indeed, the diagnoses can be subjective for many reasons for example the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Hence, for enhancing the level of diagnosis to guide clinicians, computer-aided diagnosis systems will be needed. In this paper, we put forward to develop a structure to classify pneumonia from chest X-ray images using a Convolutional Neural Network (CNN) and residual network architecture. Clearly, to determine if a person is infected with pneumonia or not, we used two well-known CNN pre-trained models (ResNet50 and ResNet101), with multi-class Support Vector Machine (SVM) to classify and transfer learning from the pre-trained CNN models to extract and classify features. Thus, the proposed framework takes an X-ray image size of 224 x 224 pixels as an input and gives the final prediction Normal or Pneumonia. The experimental results showed that the classification models proved to be effective, with an accuracy range of 97% to 98.3%. More precisely, the image extraction features using Resnet50 + SVM and Transfer Learning + Resnet50 methods achieve the highest performance of Accuracy of 98.3% and 97.8%, respectively.