Detection of Coronavirus (COVID-19) Associated Pneumonia Based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model Using Chest X-ray Dataset

The COVID-19 coronavirus is one of the devastating viruses according to the world health organization. This novel virus leads to pneumonia, which is an infection that inflames the lungs’ air sacs of a human. One of the methods to detect those inflames is by using x-rays for the chest. In this paper, a pneumonia chest x-ray detection based on generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a limited dataset will be presented. The use of GAN positively affects the proposed model robustness and made it immune to the overfitting problem and helps in generating more images from the dataset. The dataset used in this research consists of 5863 X-ray images with two categories: Normal and Pneumonia. This research uses only 10% of the dataset for training data and generates 90% of images using GAN to prove the efficiency of the proposed model. Through the paper, AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to detect the pneumonia from chest x-rays. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models proved it is efficiency according to testing accuracy measurement. The research concludes that the Resnet18 is the most appropriate deep transfer model according to testing accuracy measurement and achieved 99% with the other performance metrics such as precision, recall, and F1 score while using GAN as an image augmenter. Finally, a comparison result was carried out at the end of the research with related work which used the same dataset except that this research used only 10% of original dataset. The presented work achieved a superior result than the related work in terms of testing accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Intelligent Cloud Computing Context-Aware Model for Remote Monitoring COVID-19 Patients Using IoT Technology

This chapter proposes and investigates an intelligent context-aware model that adopts a hybrid architecture with both local and cloud-based components to monitor patients, particularly COVID-19 patients with mild symptoms while they are in their homes. The cloud-based part of the system makes storing and processing easier, especially that the data generated by ambient assisted living systems are huge, particularly with patients suffering from chronic diseases and require more frequent readings. On the other hand, the local part of the system monitors the patients in case of any sudden interruptions on the internet or any failure in the cloud system. The proposed model uses context-aware techniques by monitoring different physiological signals, surrounding ambient conditions, along with the patient activities simultaneously to build a better understanding of the health status of the COVID-19 patient in real- time, as the system will help doctors to detect if the patient has symptoms of the COVID-19. The results obtained experimentally prove how our proposed model can effectively monitor patients and detect emergencies accurately in imbalanced datasets through a case study on a patient with normotensive disorder.

Deep Learning Based Early Diagnosis for SARS-CoV-2 Using Chest X-Ray Images

Although Vaccines seem like the first ray of hope in a long time, it is still far too early to assume that this pandemic calamity is over. The corona virus is a persistent and highly contagious disease which can evolve faster than most self-acclaimed SoundCloud Rappers’ dying careers. Fortunately, computed tomography (CT) and X-Ray chest images have been proven to be very effective in diagnosing pneumonia. Although, CT scans are more accurate, they are slower, less available and more expensive than X-Ray. Chest X-Ray diagnosis requires a highly experienced medical expert, though. The popularity of Deep Learning has been sky rocketing ever since 2012’s ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Accordingly, deep learning techniques can provide an alternate computer aided diagnosis given that enough data is available for the machine learning process. This paper proposes 2 early COVID19 diagnosis deep learning models through transfer learning using frontal X-ray Chest Images, one for binary (as COVID19 and Normal) and another for multi-class (as COVID19, Normal and Viral Pneumonia) classification. The models are proposed after a comparative study on the performance of several state of the art Convolutional Neural Networks is made on both classification types. The images’ quality is first enhanced with Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and augmented. After having its Hyper Parameter optimized, each model is fine tuned to fit the data. The recall, precision, specificity, f1 Score, and accuracy are used to evaluated the performance of the models. The results indicate that the fined tuned VGG16 performed the best in multi-class classification with 96.7% and 97.8% COVID19 f1 score and testing accuracy respectively. In binary classification, ResNet50 displayed superior results with COVID19 f1 score and testing accuracy of 96.4% and 98.3% respectively. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Monitoring COVID-19 disease using big data and artificial intelligence-driven tools

With the daily huge growth in the number of confirmed COVID-19 cases, COVID-19 extremely threatens public health, countries’ economic, social life, and the international relations around the world. The accurate diagnosis based on a huge amount of data has become a serious issue that effect the disease control, especially in the widespread countries. To monitor COVID-19, big data analytics tools and Artificial Intelligence (AI) techniques play a significant role in many aspects. The integration between both technologies will help healthcare workers for early and accurately diagnose COVID-19 cases. In addition, the strategic planning for crisis management is supported by aggregation of big data to be use in the epidemiologic directions. Moreover, AI and big data driven tools presents visualization for COVID-19 outbreak information that help in detecting risk allocation and regional transmissions. In this chapter, a review of recent works related to COVID-19 containment using AI and big data techniques is introduced, showing their main findings and limitations to make it easy for researchers to investigate new techniques that will help in COVID-19 pandemic. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic's additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization algorithm (WOA) to select the most relevant patient signs. A set of experiments validated AIMDP performance. It demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic (AUC-ROC) curve, positive predictive value (PPV), negative predictive rate (NPR) and negative predictive value (NPV). AIMDP was applied to a dataset of hundreds of real data and CT images, and it was found to achieve 96% AUC for diagnosing COVID-19 and 98% for overall accuracy. The results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models.