title: ÐÐ¾Ð²ÑÑÐµÐ½Ð¸Ðµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÐºÐ¾Ð»ÑÐ¿Ð¾ÑÐºÐ¾Ð¿Ð¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¿ÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
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: Improving the efficiency of colposcopic image processing using deep learning methods
Creators: name: ÐÐ¸Ð»ÐµÐ½Ð¸Ð½Ð°, ÐÐ°ÑÑÑ
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ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ
ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÑ
ÐºÐ¾Ð»ÑÐ¿Ð¾ÑÐºÐ¾Ð¿Ð¸Ñ
ÑÐ°Ðº ÑÐµÐ¹ÐºÐ¸ Ð¼Ð°ÑÐºÐ¸
Ð¿ÐµÑÐµÐ½Ð¾Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½Ð°Ñ Ð¿Ð»Ð°ÑÑÐ¾ÑÐ¼Ð°
deep learning
data augmentation
transformers
colposcopy
cervical cancer
transfer learning
computational platform

description: Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð¾ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÐ¾Ð² Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÐºÐ¾Ð»ÑÐ¿Ð¾ÑÐºÐ¾Ð¿Ð¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. Ð¡Ð²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑ Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²ÐµÐ½Ð½ÑÑ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ, ÑÑÐ¾ Ð´ÐµÐ»Ð°ÐµÑ Ð¸Ñ Ð¸Ð´ÐµÐ°Ð»ÑÐ½Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ð¼Ð¸ Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð²Ð¸Ð·ÑÐ°Ð»ÑÐ½ÑÑ Ð´Ð°Ð½Ð½ÑÑ. ÐÑÐ¾Ð¼Ðµ ÑÐ¾Ð³Ð¾, Ð¾Ð½Ð¸ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ ÑÐ¿ÑÐ°Ð²Ð»ÑÑÑÑÑ Ñ ÑÐµÐ³ÑÐ»ÑÑÐ¸Ð·Ð°ÑÐ¸ÐµÐ¹ Ð¸Ð·-Ð·Ð° ÑÐ²Ð¾ÐµÐ¹ ÑÑÑÑÐºÑÑÑÑ, ÑÑÐ¾ Ð¿ÑÐµÐ´Ð¾ÑÐ²ÑÐ°ÑÐ°ÐµÑ Ð¿ÐµÑÐµÐ¾Ð±ÑÑÐµÐ½Ð¸Ðµ. Ð¢ÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑÑ, Ð² ÑÐ²Ð¾Ñ Ð¾ÑÐµÑÐµÐ´Ñ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ñ Ð½Ð° Ð¼ÐµÑÐ°Ð½Ð¸Ð·Ð¼Ðµ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ´ÐµÐ»ÑÑÑ Ð±Ð¾Ð»ÑÑÐµ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ Ð²Ð°Ð¶Ð½ÑÐ¼ Ð°ÑÐ¿ÐµÐºÑÐ°Ð¼ Ð² Ð´Ð°Ð½Ð½ÑÑ, Ð¸Ð³Ð½Ð¾ÑÐ¸ÑÑÑ Ð¼ÐµÐ½ÐµÐµ Ð²Ð°Ð¶Ð½ÑÐµ. Ð Ð°Ð±Ð¾ÑÐ° Ð±ÑÐ»Ð° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð² ÑÑÐµÐ´Ðµ Spyder Ð½Ð° Ð±Ð°Ð·Ðµ ÑÑÐµÐ¹Ð¼ÑÐ¾ÑÐºÐ° PyTorch. ÐÑÐ»Ð° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ñ ÑÐµÐ»ÑÑ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐ°Ð±Ð¾ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹. Ð Ð¸ÑÑÐ¾Ð´Ð½ÑÐ¼ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÐ¼ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ ÑÐ¸Ð»ÑÑÑÑ Ð´Ð»Ñ Ð¸Ð¼Ð¸ÑÐ°ÑÐ¸Ð¸ Ð¸ÑÐºÐ°Ð¶ÐµÐ½Ð¸Ð¹, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð²Ð¾Ð·Ð½Ð¸ÐºÐ°ÑÑ Ð² Ð¿ÑÐ¾ÑÐµÑÑÐµ ÑÐµÐ°Ð»ÑÐ½Ð¾Ð¹ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¾Ð¹ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸. ÐÑÐ¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð±ÑÐ» ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð¿Ð¾Ð´ÑÐ¾Ð´, Ð½Ð°Ð·ÑÐ²Ð°ÐµÐ¼ÑÐ¹ Ð¿ÐµÑÐµÐ½Ð¾ÑÐ¾Ð¼ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐ°Ð½Ð½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð¾Ð±Ð¾Ð¹ÑÐ¸ Ð²ÑÑÐ¾ÐºÐ¸Ðµ ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸Ñ Ðº Ð½Ð°Ð±Ð¾ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¼Ð¾ÑÐ½Ð¾ÑÑÐ¸. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð»Ð° Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½Ð°Ñ Ð¿Ð»Ð°ÑÑÐ¾ÑÐ¼Ð° NVIDIA Jetson Orin NX, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¾Ð±Ð»Ð°Ð´Ð°ÐµÑ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ð¼Ð¸ ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¼Ð¸ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ°Ð¼Ð¸ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ ÑÐµÐºÑÑÐ¸Ñ Ð·Ð°Ð´Ð°Ñ Ð¸ Ð¾ÑÐ½Ð¾ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð½ÐµÐ²ÑÑÐ¾ÐºÐ¾Ð¹ ÑÑÐ¾Ð¸Ð¼Ð¾ÑÑÑÑ.
description: As a result of the research, a comparison of convolutional neural networks and transformers was made to increase the efficiency of colposcopic image classification. Convolutional neural networks use spatial information, which makes them perfectly suitable for analyzing visual data. Moreover, they effectively manage regularization due to their structure, preventing overfitting. Transformers, in turn, are based on an attention mechanism that allows the model to pay more attention to important aspects in the data, ignoring less important ones. The work was carried out in the Spyder environment based on the PyTorch framework. Data augmentation was carried out to improve the efficiency of the models. Various filters were applied to the original images to simulate distortions that occur in the process of real medical diagnostics. A training method called transfer learning was implemented when training models. This method allows you to bypass high requirements for the dataset and computational power. As a result of analyzing the performance of the models, the NVIDIA Jetson Orin NX computational platform was chosen, which has suitable technical characteristics for solving current tasks and a relatively low cost.