| Autor: |
Pranshav Gajjar, Naishadh Mehta, Pooja Shah |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Computer Science Journal of Moldova, Vol 30, Iss 2(89), Pp 214-222 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1561-4042 |
| DOI: |
10.56415/csjm.v30.12 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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