ITM Web Conf.
Volume 59, 2024II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|Number of page(s)
|Data Mining, Machine Learning and Patern Recognition
|25 January 2024
Generation of a text description of weakly structured optical coherence tomography images
Research Institute of Digital Intelligent Technologies, Orenburg State University,
2 S. Fyodorov eye microsurgery federal state institution, Orenburg, Russia
* Corresponding author: firstname.lastname@example.org
Computer vision methods help to automate and improve processes in the field of medicine. In the field of ophthalmology, computer vision algorithms can be used to analyze images obtained using optical coherence tomography OCT, to identify pathologies and changes in the structure of the eye, however, due to the heterogeneity of patterns and configurations of tomographs, a comprehensive solution is needed. Within the framework of this work, an approach to the construction of a system for generating a text description of DICOM images using artificial intelligence methods is presented. To build a system of automatic description of anatomical properties and pathologies, a set of models for detection and classification was built on the OCT image. Data augmentation was performed for the task of recognizing areas with retinal slices in the OCT image. The computational experiment of constructing classification models showed recognition accuracy from 0.75 to 0.93 according to the balanced accuracy metric. Based on the developed models, a web service has been developed to demonstrate the functionality, which provides a report on finding 11 tags on an OCT scan.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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