COVID-19 pneumonia level detection using deep learning algorithm and transfer learning.

The first COVID-19 confirmed case was reported in Wuhan, China, and spread across the globe with an unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, developing smart, fast, and efficient detection techniques is significant. To this end, we have developed an Artificial Intelligence engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases; in the first phase, we calculate the volume and density of lesions and opacities of the CT scan images of the confirmed COVID-19 patient using Morphological approaches. The second phase classifies the pneumonia level of the confirmed COVID-19 patient. We use a modified Convolution Neural Network (CNN) and k-Nearest Neighbor; we also compared the results of both models to the other classification algorithms to precisely classify lung inflammation. The experiments show that the CNN model can provide testing accuracy up to 95.65% compared with exiting classification techniques. The proposed system in this work can be applied efficiently to CT scan and X-ray image datasets. Also, in this work, the Transfer Learning technique has been used to train the pre-trained modified CNN model on a smaller dataset than the original dataset; the modified CNN achieved 92.80% of testing accuracy for detecting pneumonia on chest X-ray images for the relatively extensive dataset.

Prediction of global spread of COVID-19 pandemic: a review and research challenges.

Since the initial reports of the Coronavirus surfacing in Wuhan, China, the novel virus currently without a cure has spread like wildfire across the globe, the virus spread exponentially across all inhabited continent, catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyze and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modeling tools to the latest artificial intelligence technology, the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response-whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance, including lockdowns. On the one hand, this paper shows what accuracy of research brings to enable fighting this disease; while on the other hand, it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt to compile different research methods and comparing their accuracy in predicting the spread of COVID-19.