COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks

by   Julianna Antonchuk, et al.

In the research, we developed a computer vision solution to support diagnostic radiology in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. The chest radiograph appearance of COVID-19 pneumonia is thought to be nonspecific, having presented a challenge to identify an optimal architecture of a convolutional neural network (CNN) that would classify with a high sensitivity among the pulmonary inflammation features of COVID-19 and non-COVID-19 types of pneumonia. Rahman (2021) states that COVID-19 radiography images observe unavailability and quality issues impacting the diagnostic process and affecting the accuracy of the deep learning detection models. A significant scarcity of COVID-19 radiography images introduced an imbalance in data motivating us to use over-sampling techniques. In the study, we include an extensive set of X-ray imaging of human lungs (CXR) with COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers to achieve an extensible and accurate CNN model. In the experimentation phase of the research, we evaluated a variety of convolutional network architectures, selecting a sequential convolutional network with two traditional convolutional layers and two pooling layers with maximum function. In its classification performance, the best performing model demonstrated a validation accuracy of 93 Learning service to perform network experimentation and solution deployment. The auto-scaling compute clusters offered a significant time reduction in network training. We would like to see scientists across fields of artificial intelligence and human biology collaborating and expanding on the proposed solution to provide rapid and comprehensive diagnostics, effectively mitigating the spread of the virus


page 3

page 4


Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images

By the start of 2020, the novel coronavirus disease (COVID-19) has been ...

Enhancing COVID-19 Diagnosis through Vision Transformer-Based Analysis of Chest X-ray Images

The advent of 2019 Coronavirus (COVID-19) has engendered a momentous glo...

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

The reliable and rapid identification of the COVID-19 has become crucial...

A bone suppression model ensemble to improve COVID-19 detection in chest X-rays

Chest X-ray (CXR) is a widely performed radiology examination that helps...

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays

We demonstrate use of iteratively pruned deep learning model ensembles f...

Please sign up or login with your details

Forgot password? Click here to reset