Collaborating Authors

COVID-19 detection in CT and CXR images using deep learning models - Biogerontology


Infectious diseases pose a threat to human life and could affect the whole world in a very short time. Corona-2019 virus disease (COVID-19) is an example of such harmful diseases. COVID-19 is a pandemic of an emerging infectious disease, called coronavirus disease 2019 or COVID-19, caused by the coronavirus SARS-CoV-2, which first appeared in December 2019 in Wuhan, China, before spreading around the world on a very large scale. The continued rise in the number of positive COVID-19 cases has disrupted the health care system in many countries, creating a lot of stress for governing bodies around the world, hence the need for a rapid way to identify cases of this disease. Medical imaging is a widely accepted technique for early detection and diagnosis of the disease which includes different techniques such as Chest X-ray (CXR), Computed Tomography (CT) scan, etc.

Does Imagenet Pretraining work for Chest Radiography Images(COVID-19)?


An enemy with which we are befuddled. And unless you were living under a rock for the past couple of months(like Jared Leto), you know what I'm talking about – COVID-19. Whether you turn on the news, or scroll through social media, the majority of information that you take in nowadays is about the SARS-COV2 virus, or the Novel Corona Virus. But among all the negativity, there was a sliver of light shining through. When faced with a common enemy, mankind united across borders(for the most part; there are bad apples always) to help each other tide over the current assault. Scientists, who are the heroes of the day, doubled down to find a cure, vaccine, and a million other things which helps in the battle against COVID-19.

CoroNet: A Deep Neural Network for Detection and Diagnosis of Covid-19 from Chest X-ray Images Machine Learning

The novel Coronavirus also called Covid-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting Covid-19 cases using chest X-rays. Therefore, in this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect Covid-19 infection from chest X-ray images. The deep model called CoroNet has been trained and tested on a dataset prepared by collecting Covid-19 and other chest pneumonia X-ray images from two different publically available databases. The experimental results show that our proposed model achieved an overall accuracy of 89.5%, and more importantly the precision and recall rate for Covid-19 cases are 97% and 100%. The preliminary results of this study look promising which can be further improved as more training data becomes available. Overall, the proposed model substantially advances the current radiology based methodology and during Covid-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of Covid-19 cases.

Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review Artificial Intelligence

The emergence of COVID-19 has necessitated many efforts by the scientific community for its proper management. An urgent clinical reaction is required in the face of the unending devastation being caused by the pandemic. These efforts include technological innovations for improvement in screening, treatment, vaccine development, contact tracing and, survival prediction. The use of Deep Learning (DL) and Artificial Intelligence (AI) can be sought in all of the above-mentioned spheres. This paper aims to review the role of Deep Learning and Artificial intelligence in various aspects of the overall COVID-19 management and particularly for COVID-19 detection and classification. The DL models are developed to analyze clinical modalities like CT scans and X-Ray images of patients and predict their pathological condition. A DL model aims to detect the COVID-19 pneumonia, classify and distinguish between COVID-19, Community-Acquired Pneumonia (CAP), Viral and Bacterial pneumonia, and normal conditions. Furthermore, sophisticated models can be built to segment the affected area in the lungs and quantify the infection volume for a better understanding of the extent of damage. Many models have been developed either independently or with the help of pre-trained models like VGG19, ResNet50, and AlexNet leveraging the concept of transfer learning. Apart from model development, data preprocessing and augmentation are also performed to cope with the challenge of insufficient data samples often encountered in medical applications. It can be evaluated that DL and AI can be effectively implemented to withstand the challenges posed by the global emergency

Transfer Learning: COVID-19 from Chest X-Rays Classifier


The Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. (WHO, 2020). While most persons with COVID-19 recover and return to normal health, some patients can have symptoms that can last for weeks or even months after recovery from acute illness.