A statistical and deep learning-based daily infected count prediction system for the coronavirus pandemic.

We present new data analytics-based predictions results that can help governments to plan their future actions and also help medical services to be better prepared for the future. Our system can predict new corona cases with 99.82% accuracy using susceptible infected recovered (SIR) model. We have predicted the results of new COVID cases per day for dense and highly populated country i.e. India. We found that traditional statistical methods will not work efficiently as they do not consider the limited population in a particular country. Using the data analytics-based curve we predicted four most likely possibilities for the number of new cases in India. Hence, we expect that the results mentioned in the manuscript help people to better understand the progress of this disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s12065-021-00600-2.

ECG Paper Record Digitization and Diagnosis Using Deep Learning.

PURPOSE: Electrocardiogram (ECG) is one of the most essential tools for detecting heart problems. Till today most of the ECG records are available in paper form. It can be challenging and time-consuming to manually assess the ECG paper records. Hence, automated diagnosis and analysis are possible if we digitize such paper ECG records. METHODS: The proposed work aims to convert ECG paper records into a 1-D signal and generate an accurate diagnosis of heart-related problems using deep learning. Camera-captured ECG images or scanned ECG paper records are used for the proposed work. Effective pre-processing techniques are used for the removal of shadow from the images. A deep learning model is used to get a threshold value that separates ECG signal from its background and after applying various image processing techniques threshold ECG image gets converted into digital ECG. These digitized 1-D ECG signals are then passed to another deep learning model for the automated diagnosis of heart diseases into different classes such as ST-segment elevation myocardial infarction (STEMI), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and T-wave abnormality. RESULTS: The accuracy of deep learning-based binarization is 97%. Further deep learning-based diagnosis approach of such digitized paper ECG records was having an accuracy of 94.4%. CONCLUSIONS: The digitized ECG signals can be useful to various research organizations because the trends in heart problems can be determined and diagnosed from preserved paper ECG records. This approach can be easily implemented in areas where such expertise is not available. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40846-021-00632-0.

Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening.

In today's world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. When the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into four classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on a severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG-16 with a test accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with a test accuracy of 98.9 %, whereas the ResNet-18 worked best for severity classification achieving a test accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42979-021-00695-5.

Diagnosis of COVID-19 using CT scan images and deep learning techniques.

Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over the world. There is no vaccine available for this virus yet. Fast and accurate COVID-19 screening is possible using computed tomography (CT) scan images. The deep learning techniques used in the proposed method is based on a convolutional neural network (CNN). Our manuscript focuses on differentiating the CT scan images of COVID-19 and non-COVID 19 CT using different deep learning techniques. A self-developed model named CTnet-10 was designed for the COVID-19 diagnosis, having an accuracy of 82.1%. Also, other models that we tested are DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG-19. The VGG-19 proved to be superior with an accuracy of 94.52% as compared to all other deep learning models. Automated diagnosis of COVID-19 from the CT scan pictures can be used by the doctors as a quick and efficient method for COVID-19 screening.

Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning.

The Coronavirus Disease-2019 (COVID-19) pandemic persists to have a mortifying impact on the health and well-being of the global population. A continued rise in the number of patients testing positive for COVID-19 has created a lot of stress on governing bodies across the globe and they are finding it difficult to tackle the situation. We have developed an outbreak prediction system for COVID-19 for the top 10 highly and densely populated countries. The proposed prediction models forecast the count of new cases likely to arise for successive 5 days using 9 different machine learning algorithms. A set of models for predicting the rise in new cases, having an average accuracy of 87.9%  ± 3.9% was developed for 10 high population and high density countries. The highest accuracy of 99.93% was achieved for Ethiopia using Auto-Regressive Moving Average (ARMA) averaged over the next 5 days. The proposed prediction models used by us can help stakeholders to be prepared in advance for any sudden rise in outbreak to ensure optimal management of available resources.