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Ultima descărcare din IBN: 2023-10-23 18:26 |
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004.89:61 (10) |
Inteligență artificială (311) |
Științe medicale. Medicină (11223) |
SM ISO690:2012 GAJJAR, Pranshav, MEHTA, Naishadh, SHAH, Pooja. Quadruplet loss and SqueezeNets for Covid-19 detection from Chest-X rays. In: Computer Science Journal of Moldova, 2022, nr. 2(89), pp. 214-222. ISSN 1561-4042. DOI: https://doi.org/10.56415/csjm.v30.12 |
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Computer Science Journal of Moldova | ||||||
Numărul 2(89) / 2022 / ISSN 1561-4042 /ISSNe 2587-4330 | ||||||
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DOI:https://doi.org/10.56415/csjm.v30.12 | ||||||
CZU: 004.89:61 | ||||||
MSC 2010: 68R10, 68Q25, 05C35, 05C05. | ||||||
Pag. 214-222 | ||||||
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Rezumat | ||||||
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 UNet, 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 (TDistributed 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. |
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Cuvinte-cheie COVID-19, Deep Learning applications, Lung Segmentation, X-Rays-based prediction |
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