Analysis of Medical Images Classification Methods: the Case of Neutrophil Nuclei

Anna V. Neupokoeva (Login required)
Samara State Medical University, Russian Federation

Semen A. Naydenov
Irkutsk State Medical University, Russian Federation

Elena V. Shevchenko
Irkutsk State Medical University, Russian Federation


Paper #8811 received 5 Mar 2023; revised manuscript received 27 May 2023; accepted for publication 10 Jun 2023; published online 9 Jul 2023.

Abstract

A comparative analysis of methods for processing and classification of medical images was exemplified by neutrophil nuclei digital images. Special consideration was given to three methods: 1) measuring the fractal dimension of neutrophil nuclei to determine their functional state; 2) selecting the neutrophil nucleus contours and calculating their characteristics; 3) using a neural network to classify neutrophils according to the maturity degree. When using a neural network trained on the initial data, the classification accuracy was 60%, whereas after expanding the dataset by modifying the original images, the accuracy reached 85%, although the result was not stable. The combination of contours selection of the target objects in the image, the calculation of the numerical characteristics of these contours, and classification using deep learning methods achieves the accuracy of 72–73%, whereas the accuracy deviation does not exceed 5.6%.

Keywords

fractals; deep learning; neural networks; image processing; neutrophils

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