ABSTRACT
The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.
ABSTRACT
PURPOSE: Although brain magnetic resonance spectroscopy (MRS) imaging findings in adult Wilson disease (WD) have been explained in extensive details, a paucity of information currently exists regarding brain MRS imaging findings in pediatric WD. The purpose of this study was to clarify the role of brain MRS in detecting early metabolite abnormalities in children with WD. PATIENT AND METHODS: A case-controlled prospective study included 26 children with WD and 26 healthy controls. All children were subjected to examination on a 1.5 T MRI scanner. The spectra of N-acetyl aspartate (NAA), choline (Cho), and creatine (Cr), as well as the metabolite ratios of NAA/Cho, NAA/Cr, and Cho/Cr, were measured and compared between two groups. RESULTS: Eight patients revealed increased signal intensity in the basal ganglia at T1-weighted images. When compared with healthy controls, WD patients showed a significant decrease (p < 0.05) in NAA (63.8 ± 9.6 vs 97.6 ± 3.8), Cho (46.7 ± 8.9 vs 87.3 ± 4.7), Cr (44 ± 10.1 vs 81.9 ± 4.05), NAA/Cho (1.92 ± 1.2 vs 3.34 ± 0.55), NAA/Cr (1.29 ± 0.7 vs 2.46 ± 0.34), and Cho/Cr (0.78 ± 0.4 vs 2 ± 0.13). Patients complicated with liver cell failure showed a significant decrease in all previous parameters (p < 0.05) than patients without complications. Patients with mixed neurological and hepatic diseases showed a severe reduction in NAA, NAA/Cr, and NAA/Cho compared with patients with hepatic disease only. CONCLUSION: MRS in pediatric WD detects early neurological changes even with normal MRI.