In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.

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COVID-19 open source data sets: a comprehensive survey

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model.

The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.

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Efficient deep learning approach for augmented detection of Coronavirus disease.

Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.

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An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

Extensive COVID-19 X-Ray and CT Chest Images Dataset

Automated and accurate classification of magnetic resonance images (MRIs) of the brain has great importance for medical analysis and interpretation. This paper presents a hybrid optimized classification method to classify the brain tumor by classifying the given magnetic resonance brain image as normal or abnormal. The proposed system implements a gray wolf optimizer (GWO) combined with a supervised artificial neural network (ANN) classifier to achieve enhanced MRI classification accuracy via selecting the optimal parameters of ANN. The introduced GWO-ANN classification system performance is compared to the traditional neural network (NN) classifier using receiver operating characteristic analysis. Experimental results obviously indicate that the presented system achieves a high classification rate and performs much better than the traditional NN classifier.

Hybrid gray wolf optimizer-artificial neural network classification approach for magnetic resonance brain images.

This paper presents a hybrid image watermarking technique for data hiding over Internet. The idea of the proposed technique is based on fusing multiple watermark images using wavelet fusion algorithm. Then, the resultant fused watermark is embedded in the original image using hybrid DWT-SVD watermarking algorithm to produce the watermarked image. The performance of the proposed algorithm is evaluated and a comparative study is done between the hybrid DWT-SVD and SVD watermarking algorithm for single and multiple watermarks. The experimental results verify and prove that the wavelet fusion is an efficient algorithm for fusing multiple watermarks. The image watermarking technique using the hybrid DWT-SVD is more robust than that using the SVD only. The results also prove that the proposed watermarking technique improves both the capacity of the embedded information and robustness without affecting the perceptual quality of the original image. Indeed, the extraction of the fused watermark is possible in the presence of severe attacks.

C11. Hybrid Digital Image Watermarking Technique for Data Hiding

In this paper, a Fast Fuzzy processor FP is proposed. This processor, which is implemented using FPGA, has four inputs and one output with 8-bits width for each. The proposed processor is synthesized, functionally verified and implemented using Xilinx Integrated Software Environment ISE and is tested using Xilinx Spartan 3E starter kit. A PC Graphical User Interface GUI is programmed using C# programming language to select and download the parameters of the processor through the serial port communication. The proposed processor is experimentally tested through water sprinkler system example. The experimental results approve the excellent performance of the proposed processor.

Design and Implementation of a Fast General Purpose Fuzzy Processor

https://mjeer.journals.ekb.eg/article_76962_d41d8cd98f00b204e9800998ecf8427e.pdf

Fathi Abd El-Samie

Papers

Extensive COVID-19 X-Ray and CT Chest Images Dataset

Hybrid gray wolf optimizer-artificial neural network classification approach for magnetic resonance brain images.

C11. Hybrid Digital Image Watermarking Technique for Data Hiding

Design and Implementation of a Fast General Purpose Fuzzy Processor

Classification of Diabetic Retinopathy types based on Convolution Neural Network (CNN)