| Summary: | Background. Obtaining the most reliable information when performing endoscopic
examinations of the gastrointestinal tract makes it possible to detect and classify dangerous
pathologies, including oncological ones, at an early stage. This significantly reduces
mortality among patients with these types of diseases. The problem of detecting stomach
pathologies on the video data of endoscopic studies is considered. Materials and methods.
The analysis of the effectiveness of using a fairly new architecture of the EfficientDet convolutional
neural network for detecting dangerous stomach pathologies on video images of
endoscopic examinations is carried out. 54 videos of stomach studies conducted in the endoscopic
department of the Yaroslavl Regional Clinical Oncology Hospital were used to train
and test deep machine learning algorithms. Results. The results of the developed algorithm
are analyzed in comparison with the popular approach based on the architecture of the convolutional
neural network SSD300. The dependences of the values of the loss functions of
the classification subnet and the regression subnet calculated on the training sample, as well
as the values of the standard metrics F1, mAP, Precision and Recall are obtained. The
achievement of a significant superiority in the metric of average accuracy is shown while
maintaining good indicators according to the criteria of robustness and processing speed of
the video frame. Conclusions. The developed algorithm can be used as the main one in the
implementation of a neural network module for detecting pathologies on endoscopic images
of the stomach.
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