<p>The Coronavirus disease (COVID-19) is an infectious disease that primarily affects lungs. This virus has spread in almost every continent. Countries are racing to slow down the spread by testing and treating patients. To diagnose the infected people, reverse transcription-polymerase chain reaction (RT-PCR) test is used. Because of colossal demand; PCR kits are under shortage, and to overcome this; radiographic techniques such as X-rays and CT-scan can be used for diagnostic purpose. In this paper, deep learning technology is used to diagnose COVID-19 in subjects through chest CT-scan. EfficientNet deep learning architecture is used for timely and accurate detection of coronavirus with an accuracy 0.897, F1 score 0.896, and AUC 0.895. Three different learning rate strategies are used, such as reducing the learning rate when model performance stops increasing (reduce on plateau), cyclic learning rate, and constant learning rate. Reduce on plateau strategy achieved F1-score of 0.9, cyclic learning rate and constant learning rate resulted in F1-score of 0.86 and 0.82, respectively. Implementation is available at github.com/talhaanwarch/Corona_Virus/tree/master/CT_scan.</p> Read full paper!!

<p>The advent of artificial intelligence has led to a better investigation of many complex research problems from a variety of domains. Recently deep learning approaches have emerged as cutting edge AI technologies and has been proved very effective in medical research. A large number of studies have recently used deep learning in the field of medical imaging for the detection and identification of various diseases including COVID19. In this work, we have investigated an ECG based approach for detection of COVID-19 and heart diseases using deep learning. Deep learning requires a lot of data and image augmentation is a way to enhance the size of data. In this study, we specifically examined the impact of augmenting ECG images for disease detection. Our study indicates that augmentation improves the detection accuracy to a certain extent and can adversely impact beyond that. Without augmentation, we achieved accuracy and F1 score of 0.818 and 0.776 which is reduced to 0.764 and 0.768 respectively, when multiple augmentation techniques are applied.</p> Read full paper!!

<p>Due to the successful application of machine learning techniques in several fields, automated diagnosis system in healthcare has been increasing at a high rate. The aim of the study is to propose an automated skin cancer diagnosis and triaging model and to explore the impact of integrating the clinical features in the diagnosis and enhance the outcomes achieved by the literature study. We used an ensemble-learning framework, consisting of the EfficientNetB3 deep learning model for skin lesion analysis and Extreme Gradient Boosting (XGB) for clinical data. The study used PAD-UFES-20 data set consisting of six unbalanced categories of skin cancer. To overcome the data imbalance, we used data augmentation. Experiments were conducted using skin lesion merely and the combination of skin lesion and clinical data. We found that integration of clinical data with skin lesions enhances automated diagnosis accuracy. Moreover, the proposed model outperformed the results achieved by the previous study for the PAD-UFES-20 data set with an accuracy of 0.78, precision of 0.89, recall of 0.86, and F1 of 0.88. In conclusion, the study provides an improved automated diagnosis system to aid the healthcare professional and patients for skin cancer diagnosis and remote triaging.</p> Read full paper!!

Mental stress is a natural response to life activities. However, acute and prolonged stress may cause psychological and heart diseases. Heart rate variability (HRV) is considered an indicator of mental stress and physical fitness. The standard way of obtaining HRV is using electrocardiography (ECG) as the time interval between two consecutive R-peaks. ECG signal is collected by attaching electrodes on different locations of the body, which need a proper clinical setup and is costly as well; therefore, it is not feasible to monitor stress with ECG. Photoplethysmography (PPG) is considered an alternative for mental stress detection using pulse rate variability (PRV), the time interval between two successive peaks of PPG. This study aims to diagnose daily life stress using low-cost portable PPG devices instead of lab trials and expensive devices. Data is collected from 27 subjects both in rest and in stressed conditions in daily life routine. Thirty-six time domain, frequency domain, and non-linear features are extracted from PRV. Multiple machine learning classifiers are used to classify these features. Recursive feature elimination, student t-test and genetic algorithm are used to select these features. An accuracy of 72% is achieved using stratified leave out cross-validation using K-Nearest Neighbor, and it increased up to 81% using a genetic algorithm. Once the model is trained with the best features selected with the genetic algorithm, we used the trained weights for the real-time prediction of mental stress. The results show that using a low-cost device; stress can be diagnosed in real life. The proposed method enable the regular monitoring of stress in short time that help to control the occurrence of psychological and cardiovascular diseases. Read full paper!!

<p>Usage of offensive language on social media is getting more common these days, and there is a need of a mechanism to detect it and control it. This paper deals with offensive language detection in five different languages; English, Arabic, Danish, Greek and Turkish. We presented an almost similar ensemble pipeline comprised of machine learning and deep learning models for all five languages. Three machine learning and four deep learning models were used in the ensemble. In the OffensEval-2020 competition our model achieved F1-score of 0.85, 0.74, 0.68, 0.81, and 0.9 for Arabic, Turkish, Danish, Greek and English language tasks respectively.</p> Read full paper!!

<p>Coronavirus is a pandemic that affects the respiratory system causing cough, shortness of breath, and death in severe cases. Polymerase chain reaction (PCR) tests are used to diagnose coronavirus. The false-negative rate of these tests is high, so there needs a supporting method for an accurate diagnosis. CT scan provides a detailed examination of the chest to diagnose COVID, but a single CT scan comprises hundreds of slices. Expert and experienced radiologists and pulmonologists can diagnose COVID from these hundreds of slices, but this is very time-consuming. So an automatic artificial intelligence (AI) based method is required to diagnose coronavirus with high accuracy. Developing this AI-based technique requires a lot of resources and time, but once it is developed, it can significantly help the clinicians. This paper used an Automated machine learning (AutoML) technique that requires fewer resources (optimal architecture trials) and time to develop, resulting in the best diagnosis. The AutoML models are trained on 2D slices instead of 3D CT scans, and the predictions on unknown data (slices of CT scan) are aggregated to form a prediction of 3D CT scan. The aggregation process picked the most occurred case, whether COVID or non-COVID from all CT scan slices and labeled the 3D CT scan accordingly. Different thresholds are also used to label COVID or non-COVID 3D CT scans from 2D slices. The approach resulted in accuracy and F1-score of 89% and 88%, respectively. Implementation is available at github. com/talhaanwarch/mia-covid19</p> Read full paper!!

<p>The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.</p> Read full paper!!