Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed

Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data

This work was partly supported by the MINECO/ FEDER under the RTI2018-098913-B100, CV20-45250 and A-TIC-080-UGR18 projects.The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled Data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%.Spanish Government RTI2018-098913-B100 CV20-45250 A-TIC-080UGR1

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images

A thirty-one-year old pregnant woman with infiltrative cardiac masses

Primary cardiac tumors are rare in all ages. Their reported prevalence ranges from 0.001 to 0.03 percent in autopsy series. 25 percent of primary cardiac tumors are considered to be malignant, the majority of which are sarcomas. On account of the late presentation of symptoms in malignant heart masses, finding locally infiltrative tumors or systemically widespread cases at initial presentation is common. We present a case of malignant heart tumor in a thirty-one-year old woman who was first examined here after the termination of pregnancy

Modified optical flow technique for cardiac motions analysis in echocardiography images

The quantitative analysis of cardiac motions in echocardiography images is a noteworthy issue in processing of these images. Cardiac motions can be estimated by optical flow (OF) computation in different regions of image which is based on the assumption that the intensity of a moving pattern remains constant in consecutive frames. However, in echocardiographic sequences, this assumption may be violated because of unique specifications of ultrasound. There are some methodsapplying the brightness variation effect in OF. Almost all of them have presented a mathematical brightness variation model globally in the images. Nevertheless, there is not a brightness variation model for echocardiographic images in these methods. Therefore, we are looking for a method to apply brightness variations locally in different regions of the image. In this study, we proposed a method to modify ausual OF technique by considering intensity variation. To evaluate this method, we implement two other OF-based methods, one usual OF method and a modified OF method applying brightness variation as a multiplier and an offset (generalized dynamic imaging model [GDIM]) and compare them with ours. These algorithms and ours were implemented on real 2D echocardiograms. Our method resulted in more accurate estimations than two others. At last, we compared our method with expert′spoint of view and observed that three distance metrics between them was appropriately smaller than other methods. The Haussdorff distance between the estimated curve defined by the proposed method and the expert defined curve is 4.81 pixels less than this distance for Lucas-Kanade and 2.28 pixels less than GDIM

Cardiac MRI in a Patient with Coincident Left Ventricular Non-Compaction and Hypertrophic Cardiomyopathy

Left ventricular non-compaction cardiomyopathy is a rare congenital cardiomyopathy that affects both children and adults. Since the clinical manifestations are not sufficient to establish diagnosis, echocardiography is the diagnostic tool that makes it possible to document ventricular non-compaction and establish prognostic factors. We report a 47-year-old woman with a history of dilated cardiomyopathy with unknown etiology. Echocardiography showed mild left ventricular enlargement with severe systolic dysfunction (EF = 20-25%). According to cardiac magnetic resonance imaging findings non-compaction left ventricle with hypertrophic cardiomyopathy was considered, and right ventricular septal biopsy was recommended. Right ventricular endomyocardial biopsy showed moderate hypertrophy of cardiac myocytes with foci of myocytolysis and moderate interstitial fibrosis. No evidence of infiltrative deposition was seen

Diagnosing coronary artery disease via data mining algorithms by considering laboratory and echocardiography features

BACKGROUND: Coronary artery disease (CAD) is the result of the accumulation of athermanous plaques within the walls of coronary arteries, which supply the myocardium with oxygen and nutrients. CAD leads to heart attacks or strokes and is, thus, one of the most important causes of death worldwide. Angiography, an imaging modality for blood vessels, is currently the most accurate method of diagnosing artery stenosis. However, the disadvantages of this method such as complications, costs, and possible side effects have prompted researchers to investigate alternative solutions. OBJECTIVES: The current study aimed to use data analysis, a non-invasive and less costly method, and various data mining algorithms to predict the stenosis of arteries. Among many people who refer to hospitals due to chest pain, a great number of them are normal and as such do not need angiography. The objective of this study was to predict patients who are most probably normal using features with the highest correlations with CAD with a view to obviate angiography costs and complications. Not a substitute for angiography, this method would select high-risk cases that definitely need angiography. PATIENTS AND METHODS: Different features were measured and collected from potential patients in order to construct a dataset, which was later utilized for model extraction. Most of the proposed methods in the literature have not considered the stenosis of each artery separately, whereas the present study employed laboratory and echocardiographic data to diagnose the stenosis of each artery separately. The data were gathered from 303 random visitors to Rajaie Cardiovascular, Medical and Research Center. Electrocardiographic (ECG) data were studied in our previous works. The goal of this study was, therefore, to seek the accuracy of echocardiographic and laboratory features to predict CAD patients that require angiography. RESULTS: Bagging and C4.5 classification algorithms were drawn upon to analyse the data, the former reaching accuracy rates of 79.54%, 61.46%, and 68.96% for the diagnosis of the stenoses of the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA), respectively. The accuracy to predict the LAD stenosis was attained via feature selection. In the current study, features effective in the stenosis of arteries were further determined, and some rules for the evaluation of triglyceride, hemoglobin, hypertension, dyslipidemia, diabetes mellitus, and ejection fraction were extracted. CONCLUSIONS: The current study presents the highest accuracy value to diagnose the LAD stenosis in the literature