Adamov A.A., Gndoyan I.A., Dyatchina A.I., Khramov V.N. Development of a classifier of photo images of pathologies for an ultra-small data set

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https://doi.org/10.15688/mpcm.jvolsu.2023.1.3

Anton A. Adamov
Candidate of Technical Sciences, Junior Researcher at the Institute of Mathematics and Information Technology, senior lecturer at the Departments of Radiophysics,
Volgograd State University
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https://orcid.org/0000-0002-7394-0744 
Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation

Irina A. Gndoyan
Doctor of Medical Sciences, Head Of the Department of Ophthalmology,
Volgograd State Medical University
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https://orcid.org/0000-0001-7581-9473 
pl. Pavshikh Bortsov 1, 400131 Volgograd, Russian Federation

Alena I. Dyatchina
Postgraduate student, Department of Ophthalmology,
Volgograd State Medical University
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https://orcid.org/0000-0001-9632-5800 
pl. Pavshikh Bortsov 1, 400131 Volgograd, Russian Federation

Vladimir N. Khramov
Candidate of Physical and Mathematical Sciences, Associate Professor, Department of Radiophysics,
Volgograd State University
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https://orcid.org/0000-0001-8988-0929
Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation

Abstract. The purpose of the work: to create an algorithm and implement it in a software tool for classifying photographic images of pathology of the central region of the human fundus, detected by autofluorescence research, according to 8 types-patterns: normal, minimal changes, focal, spotted, linear, lace-like, reticular, speckled. Methods: machine learning algorithms (convolutional neural networks) and computer vision (histogram methods, perceptual hash algorithms). The main feature of the task: an ultra-small set of unique photoimages with an accurately diagnosed type of pathology (18 pieces). The accuracy of forecasts when solving a problem using a neural network is 12.5%. The accuracy of the predictions of the developed algorithm using a combination of histograms, perceptual hash and 1 reference photo of the normal state of the fundus is 60% when selecting the classifier parameters from a set of 1 photo for 1 pathology. When using 3 reference photos, the norm is 85%. The proposed solution can be used in medicine, ophthalmology, photonics and optics of biological tissues, machine learning for both research and educational purposes.

Key words: photo image processing, computer vision, machine learning, image classification, histogram, perceptual hash, ophthalmological diagnostics, computerization of medicine. 

Development of a classifier of photo images of pathologies for an ultra-small data set by Adamov A. A., Gndoyan I. A., Dyatchina A. I., Khramov V. N. is licensed under a Creative Commons Attribution 4.0 International License.

Citation in English: Mathematical Physics and Computer Simulation. Vol. 26 No. 1 2023, pp. 33-48

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Matematicheskaya fizika_1_2023-34-49.pdf URL: https://mp.jvolsu.com/index.php/en/component/attachments/download/1083

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