title: ÐÐµÑÐ¾Ð´Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Deep Learning Methods for Medical Image Classification
Creators: name: ÐÑÑÐµÐ¼ÐµÐ½ÐºÐ¾, ÐÐ»Ð°Ð´Ð°
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: Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
Ð¿ÐµÑÐµÐ½Ð¾Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
deep learning
medical images
transfer learning
convolutional neural networks

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸Ð·ÑÑÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ð¸ Ðº Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐµ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ð¹ Ð¿Ð¾ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ð¼ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÐ¼. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð±Ð¾Ð»ÑÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÐ¼ (transfer learning), Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÑÐ½ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð½Ð° ÑÐµÑÑÐ¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ. Ð¢ÐµÑÑÐ¾Ð²ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ, Ðº ÐºÐ¾ÑÐ¾ÑÑÐ¼ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐ»Ð¸ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÑÐ»Ð¸ ÑÐ¾Ð±Ð¾Ð¹ 2D-ÑÐ½Ð¸Ð¼ÐºÐ¸ ÑÐµÐ½ÑÑÐ°Ð»ÑÐ½ÑÑ ÑÐ°Ð·ÑÐµÐ·Ð¾Ð² ÐÐ Ð¢ Ð¸Ð· Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ ADNI Ð´Ð»Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ Ð±Ð¾Ð»ÐµÐ·Ð½Ð¸ ÐÐ»ÑÑÐ³ÐµÐ¹Ð¼ÐµÑÐ°. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð±ÑÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ VGG16, DenseNet121, ResNet152V2, Xception Ð¸ InceptionV3. ÐÑÐ»Ð¸ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð²Ð»Ð¸ÑÐ½Ð¸Ñ Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¸ Ð¸Ð·Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð½ÑÑ Ð²ÐµÑÐ¾Ð² Ð½Ð° ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹. Ð¡ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð½ÑÑ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð±ÑÐ»Ð¾ ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¾, ÑÑÐ¾ Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½Ð° Ð±Ð¾Ð»ÑÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð½Ð¾ÑÐ¼Ð°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ ÑÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾ Ð¿Ð¾Ð²ÑÑÐ°ÑÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ ÑÐ¿Ð¾ÑÐ¾Ð±ÑÑÐ²ÑÐµÑ Ð±Ð¾Ð»ÐµÐµ ÑÐ¾ÑÐ½Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð±ÑÐ»Ð¾ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¾, ÑÑÐ¾ Ð¼Ð¾Ð´ÐµÐ»Ñ VGG16 Ð¿ÑÐ¸ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ð¸ Ð²ÐµÑÐ¾Ð² Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° Ð»ÑÑÑÐ¸Ð¹ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ ÑÑÐµÐ´Ð¸ Ð²ÑÐµÑ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, Ð´Ð¾ÑÑÐ¸Ð³Ð½ÑÐ² ÑÑÐµÐ´Ð½ÐµÐ¹ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ 93.72%. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑ Ð¿Ð¾ÑÐµÐ½ÑÐ¸Ð°Ð» Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¾Ð¹ ÑÑÐµÑÐµ Ð¸ Ð¼Ð¾Ð³ÑÑ ÑÐ»ÑÐ¶Ð¸ÑÑ Ð¾ÑÐ½Ð¾Ð²Ð¾Ð¹ Ð´Ð»Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð½Ð¾Ð²ÑÑ Ð±Ð¾Ð»ÐµÐµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸.
description: This work is devoted to the study of deep learning methods as applied to the diagnosis of various diseases from medical images. In the course of the work, models based on convolutional neural networks were developed using pre-training on big image data (transfer learning), as well as a comparative analysis of their effectiveness on test data. The test data to which the models were applied were 2D central MRI scans from the ADNI Alzheimers Diagnosis dataset. The pre-trained models VGG16, DenseNet121, ResNet152V2, Xception and InceptionV3 were used in the work. The results of the influence of data preprocessing by augmentation methods and changes in pretrained weights on the quality of models were considered. With the help of the experiments, it was found that pre-training on big data and data normalization significantly increase the efficiency of models and contribute to a more accurate classification of medical images. As a result of the analysis, it was found that the VGG16 model, with increasing weights, showed the best result among all the considered models, reaching an average accuracy of 93.72%. The results of the work show the potential of using deep learning in the medical field and can serve as a basis for the development of new, more efficient methods of classification and diagnostics.