CNN-based Clinical Diagnosis and Decision Support System for Chest X-ray

Abstract

Chest radiographs are popularly used as a first-line diagnostic tool for various chest diseases. However, the correct interpretation of information while reading a radiograph is a major challenge. We aim to develop a suitable convolutional neural network (CNN) framework for the automatic detection of disease in chest radiographs. We have designed a 15-layered CNN architecture with 3 output classes: covid, normal, and pneumonia. This architecture was trained with 10,011 images. To avoid the risk of over-fitting, data augmentation techniques were implemented on training samples while dropout layers were implemented in CNN architecture. During training, we were able to obtain 98.24% accuracy on the training dataset. On the other hand, the experimental results of the test dataset indicate that the classification accuracy is 95.8%, precision is 95.83%, recall is 95.77% and the f1-score is 95.79% on average which is comparatively superior to several existing research works.