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Control in Medicine, Biology, and Ecology
Two approaches for detecting pneumonia in x-ray images: description, implementation and comparison
A. A. Pechnikova, N. Bogdanovb a Karelian Research Centre of the Russian Academy of Sciences, Petrozavodsk
b Saint Petersburg University, Saint Petersburg
Abstract:
The paper investigates two approaches for classification of x-ray images for presence of pneumonia. The first, widely used approach relies on neural networks (NN). The second approach utilises the theoretical concept of Kolmogorov complexity. The latter approach further enables the use of normalised compression distance (NCD) which defines a distance measure for arbitrary data objects, including images. Both approaches and their underlying algorithms are described in described in detail and implemented programmatically. The X-rays for this work are taken from the database of the Kaggle social network for data processing and machine learning. Optimal model parameters are found empirically. Experimental results show high accuracies for both approaches. This is a clear indication of reliability and applicability of either method for identifying the presence of pneumonia in x-ray images. The NN approach performs well when ample training data is available. The NCD method is turn applicable when training data is limited and the NN approach fails. These results provide a solid foundation for developing precise and reliable diagnostics of pneumonia, using a combination of the two approaches.
Keywords:
classification of images, convolutional neural network, normalised compression distance, X-ray processing, pneumonia.
Received: April 28, 2022 Published: September 30, 2022
Citation:
A. A. Pechnikov, N. Bogdanov, “Two approaches for detecting pneumonia in x-ray images: description, implementation and comparison”, UBS, 99 (2022), 114–134
Linking options:
https://www.mathnet.ru/eng/ubs1120 https://www.mathnet.ru/eng/ubs/v99/p114
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Abstract page: | 49 | Full-text PDF : | 13 | References: | 8 |
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