An efficient deep learning approach to detect various diseases using chest X-ray images

Abstract

We propose and demonstrate an efficient deep-learning approach to classify various diseases using chest x-ray images. The proposed system comprises several steps: image acquisition, preprocessing, and classification of various diseases. The datasets include X-ray images of various diseases such as pneumonia, COVID-19, lung opacity, and normal chest images. Raw X-ray images and the dataset from Kaggle is preprocessed using image resizing and augmentation. Finally, a network-based deep learning model is applied to classify the disease. Different CNN architectures: ResNet50, ResNet101, EfficientNet, DenseNet121, and AlexNet are investigated for the classification, and the best-performing architecture is used in the model, to design a custom-made model named X Net. By incorporating certain layers from both ResNet101 and DenseNet121. The ResNet101 and DenseNet121 models gave 94% and 92% accuracy, respectively, where the rest of the models gave lower accuracy than them. Our proposed model achieves a higher accuracy of upto 96.5%.