Quadruplet loss and SqueezeNets for Covid-19 detection from Chest-X rays
Computer Science Journal of Moldova
; 30(2):214-222, 2022.
Article
in English
| Scopus | ID: covidwho-1965236
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. © 2022 by CSJM;Pranshav Gajjar, Naishadh Mehta, Pooja Shah
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Computer Science Journal of Moldova
Year:
2022
Document Type:
Article
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