Deep learning model for binary classification of COVID-19 based on Chest X-Ray

COVID-19 is a novel virus infecting the upper respiratory tract and lungs. On a scale of the global pandemic, the number of cases and deaths had been increasing each day. Chest X-ray (CXR) images proved effective in monitoring a variety of lung illnesses, including the COVID-19 disease. In recent years, deep learning (DL) has become one of the most significant topics in the computing world and has been extensively applied in several medical applications. In terms of automatic diagnosis of COVID-19, those approaches had proven to be very effective. In this research, a DL technology based on convolution neural networks (CNN) models had been implemented with less number of layers with tuning parameters that will take less time for training for binary classification of COVID-19 based on CXR images. Experimental results had shown that the proposed model for training had achieved an accuracy of 96.68%, Recall of 94.12%, Precision of 93.49%, Specificity of 97.61%, and F1 Score of 93.8%. Those results had shown the high value of utilizing DL for early COVID-19 diagnosis, which can be utilized as a useful tool for COVID-19 screening. © 2023 IEEE.

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

The outbreak of the new coronavirus (COVID-19) has created a disaster worldwide and it became a very severe and acute disease. COVID-19 prevalence is rapidly increasing around the world. Deep learning (DL) technology had become a hot topic in the context of computing and is widely applied in various medical applications. These techniques have proven to be one of the effective tools for clinicians in the automatic diagnoses of COVID- 19. The goal of the present paper is to provide an overview of recently developed systems based on DL techniques that use various medical imaging modalities such as Computer Tomography (CT) and Chest X-Rays (CXR). This review focuses on systems that had been developed for the diagnosis of COVID-19 with the use of the DL methods, as well as the well-known datasets that are utilized for the training of those networks. Finally, the researcher reviewed 58 research papers based on different medical images. Overall, this article aims to assist experts (medical or otherwise) and technicians to understand how the DL approaches are utilized in this context and the way that they can potentially be expanded to combat COVID-19 outbreaks. © 2022 International Information and Engineering Technology Association. All rights reserved.

Human visualization system based intensive contrast improvement of the collected COVID-19 images

Enhancement and color correction of images play an important role and can be considered as one of the fundamental and basic operations in image analysis for the purpose of speeding up the diagnosis of the medical images. Improving the quality and contrast of the medical image is the basic requirement for clinicians for obtaining an accurate and accurate medical diagnosis. Thus, getting a clear X-ray image reduces the effort and time-wasting. In this study a new idea will be applied for improving image contrast of the collected COVID-19 X-ray images, this idea is based on using Wiener filter, multilevel of histogram equalization (HE) technique with OpenCV library and then using contrast limited adaptive histogram equalization (CLAHE) techniques with OpenCV library. The proposed methodology programmed in MATLAB software and then implemented using Rasperry Pi 3 model B. The size and resolution of images are different as inputted images and this difference succeeded in proving the strength of the proposed idea. The collected X-ray images have undergone experiential evaluations which clearly showed the effective performance of the proposed methodology. © 2022 Institute of Advanced Engineering and Science. All rights reserved.