Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases

Abstract

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research

“…( 2020 ) X-ray Explainable DL Model Lungs Deep learning method Sharma et al. ( 2020 ) X-ray Pre-trained DL Models Lungs Deep learning method Jain et al. ( 2020 ) X-ray ResNeXt DL Model Lungs Deep learning method Jaiswal et al.…”

COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases

“…Motivated by the success of deep learning methods in many computer vision tasks, most of the existing works for the recognition of COVID-19 infection from X-ray scans have used deep leaning methods [ 4 , 5 , 8 , 9 , 10 , 13 ].…”

“…Many Artificial Intelligence (AI) systems have proved their efficiency in medical images analysis, such as pneumonia detection [ 6 ] semantic segmentation [ 7 ]. Similarly, many AI systems based on deep learning have been proposed and their performance has shown promising results in the diagnosis of COVID-19 infection from chest X-ray images [ 4 , 5 , 8 , 9 , 10 ]. The ability of deep convolutional neural networks to extract relevant and high-level features directly from data makes them more powerful than Hand-crafted methods [ 11 ].…”

These do not Look Like Those: An Interpretable Deep Learning Model for Image Recognition

“…This type of human reasoning is accommodated by the ProtoPNet model, where comparison of image parts with learned prototypes is integral to the reasoning process of the model. Recently, some deep learning/machine learning models have been developed to classify the X-ray images of Covid-19 patients, normal people and pneumonia patients, see [1], [7], [16], [17], [19], [25], [28], [44]. A survey article is also written that summarizes the research works related to deep learning applications on COVID-19 medical image processing [2].…”