title: COVID-19 abnormalities localization on chest radiographs with transformer neural network
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: ÐÐ¾ÐºÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹ COVID-19 Ð½Ð° ÑÐµÐ½ÑÐ³ÐµÐ½Ð¾Ð³ÑÐ°Ð¼Ð¼Ð°Ñ Ð¾ÑÐ³Ð°Ð½Ð¾Ð² Ð³ÑÑÐ´Ð½Ð¾Ð¹ ÐºÐ»ÐµÑÐºÐ¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸-ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ°
Creators: name: Ð§Ð¶Ð°Ð½ Ð¡ÑÐ¾
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²
ÐÐ·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ
ÑÐµÐ½ÑÐ³ÐµÐ½Ð¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð³ÑÑÐ´Ð½Ð¾Ð¹ ÐºÐ»ÐµÑÐºÐ¸
transformer model
chest radiographs

description: Ð¢ÐµÐ¼Ð° Ð´Ð¸Ð¿Ð»Ð¾Ð¼Ð½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð¿Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¸ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¢ÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ°, ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð½Ð¾Ð²Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð»Ð¾ÐºÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ COVID-19. ÐÐ·ÑÑÐ°Ñ ÑÐµÐºÑÑÐ¸Ð¹ ÑÑÐ°ÑÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ COVID-19, Ð±ÑÐ»Ð¾ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¾, ÑÑÐ¾ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ (CNN) ÑÐ¸ÑÐ¾ÐºÐ¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÑÑÑÑ Ð² ÑÑÐ¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸, ÑÐ¾Ð³Ð´Ð° ÐºÐ°Ðº Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹-ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ¾Ð² Ð½Ðµ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð½ÑÐ¼ ÑÐ²Ð»ÐµÐ½Ð¸ÐµÐ¼. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÑÐ¾Ð³Ð¾ Ð¼Ñ ÑÐ»ÑÑÑÐ¸Ð»Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¢ÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ° Ð¸ Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð»Ð¸ Ð²ÐµÑÐ° Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¢ÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ°, Ð¿Ð¾Ð»ÑÑÐ¸Ð² ÐºÐ°ÑÑÑ ÑÐµÐ¿Ð»Ð¾Ð²Ð¾Ð¹ Ð°ÐºÑÐ¸Ð²Ð°ÑÐ¸Ð¸, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð²ÑÑÐ²Ð»ÑÐµÑ ÐºÐ»ÑÑÐµÐ²ÑÐµ Ð¾Ð±Ð»Ð°ÑÑÐ¸, Ð²Ð»Ð¸ÑÑÑÐ¸Ðµ Ð½Ð° ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ° Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¸, ÑÑÐ¾ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð·Ð°ÑÐ°Ð¶ÐµÐ½Ð½Ð¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸ ÑÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾ Ð²Ð»Ð¸ÑÑÑ Ð½Ð° Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÑÑ, ÑÑÐ¾ Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÑÐ¿ÐµÑÐ½Ð¾ ÑÐ¸ÐºÑÐ¸ÑÑÐµÑ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð·Ð°ÑÐ°Ð¶ÐµÐ½Ð½Ð¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑ Ð¸Ñ Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¾ÑÐ½Ð¾Ð²Ñ Ð´Ð»Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¾ÑÐµÐ½ÐºÐ¸. ÐÐ°Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Kaggle COVID-19 Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð°ÑÑ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð° Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°Ð»Ð°ÑÑ Ñ Ð¼Ð¾Ð´ÐµÐ»ÑÐ¼Ð¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹, ÑÐ°ÐºÐ¸Ð¼Ð¸ ÐºÐ°Ðº VGG-16, CNN, ResNet18 Ð¸ MobileNetV2. Ð¡ÑÐµÐ´Ð¸ Ð²ÑÐµÑ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð¾Ð´ÐµÐ»Ñ Transformer Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° Ð»ÑÑÑÐ¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ, Ð´Ð¾ÑÑÐ¸Ð³Ð½ÑÐ² ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¸ Ð¿Ð¾Ð»Ð½Ð¾ÑÑ 95,34% Ð¸ 94,59% ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÐµÐ½Ð½Ð¾. ÐÐ¾Ð»ÐµÐµ ÑÐ¾Ð³Ð¾, Ð¾Ð½ Ð¿ÑÐ¾Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð» Ð²ÑÑÐ¾ÐºÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¸ Ð½Ð¸Ð·ÐºÐ¸Ð¹ ÑÑÐ¾Ð²ÐµÐ½Ñ Ð¾ÑÐ¸Ð±Ð¾ÑÐ½Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿ÑÐ¸ Ð»Ð¾ÐºÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð°Ð½Ð¾Ð¼Ð°Ð»ÑÐ½ÑÑ Ð¾Ð±Ð»Ð°ÑÑÐµÐ¹ COVID-19. Ð¢Ð°ÐºÐ¸Ð¼ Ð¾Ð±ÑÐ°Ð·Ð¾Ð¼, ÑÑÐ¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐµÐ´Ð¾ÑÑÐ°Ð²Ð»ÑÐµÑ Ð½Ð¾Ð²ÑÐ¹ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½Ñ Ð´Ð»Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ COVID-19, ÑÐ°ÑÑÐ¸ÑÑÐµÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¢ÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ° Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¸ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÑ Ð¾ÑÐ½Ð¾Ð²Ñ Ð´Ð»Ñ Ð±ÑÐ´ÑÑÐ¸Ñ ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÑÑÑÐ¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹.
description: The subject of the graduate qualification work to apply and optimize the Transformer model, constructing a novel system for COVID-19 image recognition and localization. By investigating the current research status in the field of COVID-19 image recognition, it was found that Convolutional Neural Networks (CNNs) are widely applied in this area, whereas the use of Transformer models is not common. Based on this, we improved the Transformer model and visualized the attention weights of the Transformer model, obtaining a heat activation map that reveals the key areas affecting the models prediction results. The experimental results indicated that the features of the infected area significantly impact the models predictions, demonstrating that the model successfully captures the features of the infected area and uses them as the basis for evaluation results.In the study, chest X-ray images from the Kaggle COVID-19 Radiology database were used to train and test the model, and the performance was compared with image recognition models such as VGG-16, CNN, ResNet18, and MobileNetV2. Among all the comparative models, the Transformer model performed the best, achieving an accuracy and recall rate of 95.34% and 94.59% respectively. Moreover, it demonstrated high accuracy and a low misclassification rate in localizing abnormal areas of COVID-19. In summary, this research provides a novel tool for the diagnosis of COVID-19, broadens the application of the Transformer model in the field of medical image recognition, and offers a reference for future related studies.