Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

In the last three years, the coronavirus (COVID-19) pandemic put healthcare systems worldwide under tremendous pressure. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing many diseases (for example, COVID-19). Recently, intelligent systems (Machine Learning (ML) and Deep Learning (DL)) have been widely utilized to identify COVID-19 from other upper respiratory diseases (such as viral pneumonia and lung opacity). Nevertheless, identifying COVID-19 from the CXR images is challenging due to similar symptoms. To improve the diagnosis of COVID-19 using CXR images, this article proposes a new deep neural network model called Fast Hybrid Deep Neural Network (FHDNN). FHDNN consists of various convolutional layers and various dense layers. In the beginning, we preprocessed the dataset, extracted the best features, and expanded it. Then, we converted it from two dimensions to one dimension to reduce training speed and hardware requirements. The experimental results demonstrate that preprocessing and feature expansion before applying FHDNN lead to better detection accuracy and reduced speedy execution. Furthermore, the model FHDNN outperformed the counterparts by achieving an accuracy of 99.9%, recall of 99.9%, F1-Score has 99.9%, and precision of 99.9% for the detection and classification of COVID-19. Accordingly, FHDNN is more reliable and can be considered a robust and faster model in COVID-19 detection. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Detection Covid-19 Infection of Lung CT Scan Slices Images Based on a Transfer Learning and GRAD-CAM

The long timeline of polymerase chain reaction (PCR) tests and the lack of test tool kits in many hospitals lead to fast infection according to the slow diagnosis. The Various experiences of radiologists cause deferent in accurately detection lessons. This research suggested and designed a model based on utilizing the deep learning (DL) algorithms to detect and visualize the infection of covid-19 patients. This work shows how various convolution layers of convolution neural networks (CNN) extract different types of features, which helps us understand how CNN steadily gains spatial information in each layer. As a result, every transition focuses on the region of interest. Understanding how CNN identifies and locates distinct infection areas in an image is easier with a heat map of activation. A gradient class activation map (Grad-CAM) was used to visualize the infected area in the lungs. Transfer learning such as Resnet50, VGG16, and inception V3 have been applied to the dataset to detect the infection area. The result of these models reached an accuracy of 71.01%, 83.51%, and 94.93%, respectively. The VGG16 has been manipulated because the model consumes less training time than the other models to solve the problem. Manipulating on VGG16 has been accomplished to achieve acceptable accuracy. The tuning on the last three layers of VGG16 architecture (dense layers) replaces them with two layers (global average layer and dense layer). The dense layer that is added deals with binary classification problems depending on the sigmoid function. This tuning serves the current study by speeding up the model's prediction and increasing the accuracy. The result of the testing reached 0.9683% of accuracy and 0.0931 loss function without augmentation. This work depends on the current dataset because it contains the mask of lung and infection, which was obtained to localize the infection area. The obtained result proved that the system could help the radiologist accommodate the pandemic. © 2022 IEEE.

Analyzing Chest X-Ray to Detect the Evidence of Lung Abnormality Due to Infectious Disease

Analyzing chest X-ray is the must especially when are required to deal of infectious disease outbreak, and COVID-19. The COVID-19 pandemic has had a large effect on almost every facet of life. As COVID-19 was a disease only discovered in recent history, there is comparatively little data on the disease, how it is detected, and how it is cured. Deep learning is a powerful tool that can be used to learn to classify information in ways that humans might not be able to. This allows computers to learn on relatively little data and provide exceptional results. This paper proposes a customized convolutional neural network (CNN) for the detection of COVID-19 from chest X-rays called basicConv. This network consists of five sets of convolution and pooling layers, a flatten layer, and two dense layers with a total of approximately 9 million parameters. This network achieves an accuracy of 95.8%, which is comparable to other high-performing image classification networks. This provides a promising launching point for future research and developing a network that achieves an accuracy higher than that of the leading classification networks. It also demonstrates the incredible power of convolution. This paper is an extension of a 2022 Honors Thesis (Henderson, Joshua Elliot, "Convolutional Neural Network for COVID-19 Detection in Chest X-Rays” (2022). Honors Thesis. 254. https://red.library.usd.edu/honors-thesis/254 ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Deep learning for Covid-19 classification using CT scan slices of lung

Since the rapid spreading of covid-19 in 2019 in the whole world, it was conceded in 2020 as a pandemic. The long timeline of PCR tests and lack of test tool kits in many hospitals leads to fast infection according to the slow diagnosis. Various experiences of radiologists cause deferent in accurately detection lessons. This research suggested and designed a model based on utilizing the deep learning (DL) algorithms to detect the infection of covid-19 patients. Transfer learning VGG16 has been manipulated and used to solve the problem. Manipulating on VGG16 has been accomplished to achieve acceptable accuracy. The tuning on the last three layers of VGG16 architecture (dense layers) by replacing them with two layers (flatten layer and dense layer). The dense layer that is added deals with binary classification problems depending on the sigmoid function. This tuning serves the current study by speeding up the prediction of the model and also increasing the accuracy. A large COVID-19 CT scan slice dataset has been used to train and test the model. The result of testing reached 99.7% with a loss of 0.0085 and a validation loss of 0.0162. The obtained result proved that the system can help the radiologist accommodate the pandemic. © 2022 IEEE.