Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images

Abstract

Novel Coronavirus is deadly for humans and animals. The ease of its disper-sion, coupled with its tremendous capability for ailment and death in infected people, makes it a risk to society. The chest X-ray is conventional but hard to interpret radiographic test for initial diagnosis of coronavirus from other related infections. It bears a considerable amount of information on physiological and anatomical features. To extract relevant information from it can occasionally become challenging even for a professional radiologist. In this regard, deep-learning models can help in swift, accurate and reliable outcomes. Existing datasets are small and suffer from the balance issue. In this paper, we prepare a relatively larger and well-balanced dataset as compared to the available datasets. Furthermore, we analyze deep learning models, namely, AlexNet, SqueezeNet, DenseNet201, MobileNetV2 and InceptionV3 with numerous variations such as training the models from scratch, fine-tuning without pre-trained weights, fine-tuning along with updating pre-trained weights of all layers and fine-tuning with pre-trained weights along with applying augmentation. Our results show that fine-tuning with augmentation generates best results in pre-trained models. Finally, we have made architectural adjustments in MobileNetV2 and InceptionV3 models to learn more intricate features, which are then merged in our proposed ensemble model. The performance of our model is statistically analyzed against other models using four different performance metrics with paired two-sided t-test on 5 different splits of training and test sets of our dataset. We find that it is statistically better than its competing methods for the four metrics. Thus, the computer-aided classification based on the proposed model can assist radi-ologists in identifying coronavirus from other related infections in chest X-rays with higher accuracy. This can help in a reliable and speedy diagnosis, thereby saving valuable lives and mitigating the adverse impact on the socioeconomics of our community.

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

“…While deep networks have the advantage of training models from scratch, they suffer from overfitting on small datasets. Therefore, transfer learning-based models are very popular for COVID-19 detection [ 10 – 12 ]. The development of end-to-end integrated applications based on edge computing, on the other hand, has received relatively little attention.…”

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

“… Jin et al (2021) [ 260 ] Automatic COVID-19 diagnosis CXR CNN 1743 543 98.64 A hybrid ensemble model, including a pre-trained AlexNet as feature extractor and an SVM classifier as the classifier, has been proposed. Ahmad et al (2021) [ 261 ] Automatic COVID-19 diagnosis CXR CNN 4000 1000 98.45 Some of the existing CNN architectures with data augmentation have been used for COVID-19 diagnosis. Zhang et al (2021) [ 262 ] Automatic COVID-19 diagnosis CXR CNN 11,106 5806 – An AUC of 0.92 has been achieved for CV19-Net deep neural network architecture.…”

A depthwise separable dense convolutional network with convolution block attention module for COVID-19 diagnosis on CT scans

“…When dataset is limited, conventional shallow CNN models produce better results as compared to deeper models [ 50 ]. Therefore, AM-SdenseNet only has three dense blocks.…”