COVID-19 Detection using adopted convolutional neural networks and high-performance computing.

The COVID 19 pandemic is highly contagious disease is wreaking havoc on people's health and well-being around the world. Radiological imaging with chest radiography is one among the key screening procedure. This disease contaminates the respiratory system and impacts the alveoli, which are small air sacs in the lungs. Several artificial intelligence (AI)-based method to detect COVID-19 have been introduced. The recognition of disease patients using features and variation in chest radiography images was demonstrated using this model. In proposed paper presents a model, a deep convolutional neural network (CNN) with ResNet50 configuration, that really is freely-available and accessible to the common people for detecting this infection from chest radiography scans. The introduced model is capable of recognizing coronavirus diseases from CT scan images that identifies the real time condition of covid-19 patients. Furthermore, the database is capable of tracking detected patients and maintaining their database for increasing accuracy of the training model. The proposed model gives approximately 97% accuracy in determining the above-mentioned results related to covid-19 disease by employing the combination of adopted-CNN and ResNet50 algorithms.

LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans

Recent years have witnessed a rise in employing deep learning methods, especially convolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model. © 2023 Association for Computing Machinery.

HRCT chest in COVID-19 patients: An initial experience from a private imaging center in western India.

The COVID-19 pandemic began in late December in 2019 and has now reached to 216 countries with 1,08,42,028 confirmed cases and 5,21,277 deaths according to the WHO reports and 6,49,666 confirmed cases in india alone with 18,679 deaths (as on 04th july 2020). RT-PCR has been considered the standard test for diagnosis of COVID 19. However, there has been reported a high false negative rate. This high false negative rate increases the risk of further transmission as well as delays the timely management of suspected cases. We have conducted HRCT chest of various (200 patient case study) proven and suspected cases of COVID-19 infection in the months of April, May and June 2020. Out of 200 scanned patients with clinical complains and suspicion, positive HRCT chest findings were seen in 196 patients, showing clinical-radiological correlation and an accuracy of 98%. The sensitivity of chest CT in suggesting COVID-19 was 98.6% (146/148patients) based on positive RT-PCR results. In patients with negative RT-PCR results and high clinical suspicion, 90% (18/20) had positive chest CT findings. HRCT chest is very sensitive and accurate in picking up lung parenchymal abnormalities in laboratory negative RT-PCR cases with high clinical suspicion of COVID-19 infection and also in all symptomatic patients where RT-PCR was not done. HRCT can also be very sensitive, cost effective and time effective in screening patients with high clinical suspicion. HRCT scores over RT-PCR in giving immediate results, assessing severity of disease and prediction of prognosis. We suggest HRCT chest for detection of early parenchymal abnormalities, assessing severity of disease in all patients with clinical symptoms and suspicion of COVID infection irrespective of laboratory RT-PCR status.