Author:
Zhigalov Arthur,Lositsky Alexander,Grishina Lyubov,Bolodurina Irina
Abstract
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.
Reference8 articles.
1. Logan Y. -Y., Kokilepersaud K., Kwon G., AlRegib G., Wykoff C., Yu H., Multi-Modal Learning Using Physicians Diagnostics for Optical Coherence Tomography Classification, in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India, pp. 1–5 (2022). https://www.doi.org/10.1109/ISBI52829.2022.9761432
2. Jerry J.M., Thomas T., Pugalenthi R., Thomas T., Automatic Classification of Retinal Fundus Images for Diabetic Retinopathy Detection, in 2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI), Chennai, India, pp. 1–5 (2023). https://www.doi.org/10.1109/RAEEUCCI57140.2023.10134335
3. Jadhav A.S., Pawar R.V., Patil P.B., Segmentation and Classification of Retina Images using SVD Features, in 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT), Mysuru, India, pp. 712–716, (2021). https://www.doi.org/10.1109/ICEECCOT52851.2021.9707985
4. Renuga D.S., Gopalakrishnan G., Gayathri K., Detection of Eye Strain using Retina Medical Images through CNN, in Smart Technologies, Communication and Robotics (STCR), Sathyamangalam, India, pp. 1–5 (2021). https://www.doi.org/10.1109/STCR51658.2021.9589024
5. Kim J., Tran L., Retinal Disease Classification from OCT Images Using Deep Learning Algorithms, in 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Melbourne, Australia, pp. 1–6 (2021). https://www.doi.org/10.1109/CIBCB49929.2021.9562919