Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images

Abstract

Early detection of abnormalities in chest X-rays is essential for COVID-19 diagnosis and analysis. It can be effective for controlling pandemic spread by contact tracing, as well as for effective treatment of COVID-19 infection. In the proposed work, we presented a deep hybrid learning-based framework for the detection of COVID-19 using chest X-ray images. We developed a novel computationally light and optimized deep Convolutional Neural Networks (CNNs) based framework for chest X-ray analysis. We proposed a new COV-Net to learn COVID-specific patterns from chest X-rays and employed several machine learning classifiers to enhance the discrimination power of the presented framework. Systematic exploitation of max-pooling operations facilitates the proposed COV-Net in learning the boundaries of infected patterns in chest X-rays and helps for multi-class classification of two diverse infection types along with normal images. The proposed framework has been evaluated on a publicly available benchmark dataset containing X-ray images of coronavirus-infected, pneumonia-infected, and normal patients. The empirical performance of the proposed method with developed COV-Net and support vector machine is compared with the state-of-the-art deep models which show that the proposed deep hybrid learning-based method achieves 96.69% recall, 96.72% precision, 96.73% accuracy, and 96.71% F-score. For multi-class classification and binary classification of COVID-19 and pneumonia, the proposed model achieved 99.21% recall, 99.22% precision, 99.21% F-score, and 99.23% accuracy.

Analysis and prediction of type-2 diabetes mellitus complicated with coronary heart disease based on LSTM

“…Through the above analysis of the pathogenesis of diabetes and the harm of complications of diabetes, the prevention of diabetes in advance, especially the occurrence of type 2 diabetes, is of great significance for the timely treatment and post-intervention guidance of patients. With the integrated development of medicine and machine learning, deep learning models grow rapidly, such as Random forest (RF) [7], Convolutional neural network (CNN) [8] and Long and short term memory neural network (LSTM) [9], among which LSTM is one of the deep learning models widely used at present, and it is a variant of cyclic neural network. Through the "gate" to control adding or discarding information, to realize the function of memory or forgetting.…”

A Deep Dive into the Nexus between Digital Health and Life Sciences Amidst the COVID-19 Pandemic: An Editorial Expedition

“…All the other studies [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] have dealt with the use of DH in the life sciences, which, in addition to bioengineering, touched by all, have examined, from time to time, other categories ( Table 1 ), such as Anatomy [ 8 , 11 ], Bioinformatics [ 14 , 16 ], Cell biology [ 8 , 11 ], Neuroscience [ 8 , 12 , 15 , 17 , 20 ], Physiology [ 9 , 10 , 12 , 13 ], Population biology [ 14 , 16 , 18 , 19 ], and others that are shown in Table 1 .…”

“…An AI algorithm for the early detection of abnormalities in chest X-rays, for COVID-19 diagnostics, was proposed in [ 11 ]. It used a deep hybrid learning-based framework for the detection of COVID-19 using chest X-ray images.…”

“…The published works on the DH, reported in the collection [ 1 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ], demonstrate the usefulness of DH in connecting various branches of life sciences, especially during the pandemic. The use of DH in contact tracing has been particularly significant, with several studies reporting on its impact and factors influencing its use [ 2 , 4 , 5 , 6 , 7 ].…”

“…Additionally, DH applications have been used in biomedical diagnostics [ 8 , 11 ], physical activity [ 12 , 13 ], mental health monitoring [ 17 ], and remote healthcare interventions [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”