Quadruplet loss and SqueezeNets for Covid-19 detection from Chest-X rays

Abstract

The Coronavirus Pandemic triggered by SARS-CoV-2 has wreaked havoc on the planet and is expanding exponentially. While scanning methods, including CT scans and chest X-rays, are commonly used, artificial intelligence implementations are also deployed for COVID-based pneumonia detection. Due to image biases in X-ray data, bilateral filtration and Histogram Equalization are used followed by lung segmentation by a U-Net, which successfully segmented 83.2\% of the collected dataset. The segmented lungs are fed into a Quadruplet Network with SqueezeNet encoders for increased computational efficiency and high-level embeddings generation. The embeddings are computed using a Multi-Layer Perceptron and visualized by T-SNE (T-Distributed Stochastic Neighbor Embedding) scatterplots. The proposed research results in a 94.6\% classifying accuracy which is 2\% more than the baseline Convolutional Neural Network and a 90.2\% decrease in prediction time.

Keywords

• covid-19
• deep learning applications
• lung segmentation
• x-rays-based prediction