Augmented Reality for COVID-19 Aid Diagnosis: Ct-Scan segmentation based Deep Learning

The virus new variants of Coronavirus disease 2019 (COVID-19) continue to appear, making the situation more challenging and threatening. The COVID-19 pandemic has profoundly affected health systems and medical centres worldwide. The primary clinical tools used in diagnosing patients presenting with respiratory distress and suspected COVID-19 symptoms are radiology examinations. Recently emerging artificial intelligence (AI) technologies further strengthen the power of imaging tools and help medical specialists. This paper presents an Augmented Reality (AR) tool for COVID-19 aid diagnosis, including Computerised Tomography Ct-scans segmentation based Deep Learning, 3D reconstruction, and AR visualisation. Segmentation is a critical step in AI-based COVID-19 image processing and analysis;we use the popular segmentation networks, including classic U-Net. Quantitative and qualitative evaluation showed reasonable performance of U-Net for lung and COVID-19 lesions segmentation. The AR-COVID-19 aid diagnosis system could be used for medical education professional training and as a support visualisation and reading tool for radiologist. © 2022 IEEE.

Small-Sized Neural Network for Detecting COVID-19 from Chest X-rays

COVID-19 is a highly contagious infection that has now reached almost all countries in the world infecting over 33M and killing 1M people as of the time of writing. Therefore, it is essential to diagnose it early so that health care professionals can prevent the chance of a person spreading the virus. Because the disease often presents with respiratory symptoms, one method for detecting it is by radiology examination using chest radiography. Healthcare professionals examine the chest X-ray for abnormalities that are characteristics of those infected with COVID-19, which must be distinguished from other conditions with similar presentation such as pneumonia. This requires significant expertise, which may not be available in all parts of the world, so computer assisted diagnosis would be highly beneficial. We propose a deep neural network for extracting those abnormalities as features and classifying the infection. In this study, we examine the efficiency of small-sized deep neural network tailored for the detection of COVID-19 infection from chest X-ray (CXR) images. We designed a modified version of SqueezeNet and Capsule Network and show that even with a relatively small number of free parameters, it can achieve a competitive result while having modest hardware requirements. We use a modified version of fire modules to ensure better convergence. For our Capsule network, we used fire modules as two of its upper layers. To our knowledge, this is the first time that a fire module has been used in conjunction with capsules. Without any pretraining or transfer learning, our SqueezeNet was able to achieve an accuracy of 94.8 %, sensitivity of 88.0 %, and specificity of 98.4%. Additionally, our CapsNet achieved an accuracy of 93.8 %, sensitivity of 88.0 %, and specificity of 96.9 %. © 2020 IEEE.