Detecting Chest Diseases with Chest X-Ray Using Convolutional Neural Network

Lung diseases, such as viral pneumonia, lung cancer, and the coronavirus, have caused significant deaths worldwide in the past year. The detection of such diseases by radiologists is a challenging task. However, by applying image processing and artificial intelligence techniques, the computer can help radiologists identify chest diseases more accurately. Our research work aims to propose a methodology to detect chest diseases using convolutional Neural Network (CNN) and image processing techniques more effectively. In this paper, two predefined CNN model architectures (VGG-16, and VGG-19) are applied with Contrast Limited Adaptive Histogram Equalization (CLAHE) as a pre-processing technique in order to enhance the chest X-ray images. Moreover, Gradient Weighted Class Activation Maps (Grad-CAM) were applied to localize the affected areas in chest X-ray images. The two models are trained on the COVID-19 Radiography Dataset provided by Kaggle. For the performance evaluation, the two CNN models are investigated and evaluated with the image-enhanced technique. The experimental work demonstrates a significant improvement in performance using the CLAHE technique for both models. The results show that the enhanced VGG-16 model achieved a 97% accuracy rate and a 97% precision rate. With the consideration of X-ray image enhancement, the VGG16 model performs well and is a very promising way to diagnose chest disorders and lessen the radiologists’ workload.