Аннотирование объектов на медицинских изображениях рентгенографии грудной клетки с применением нейронных сетей
| Parent link: | Современные технологии, экономика и образование: сборник материалов II Всероссийской научно-методической конференции, г. Томск, 2-4 сентября 2020 г./ Национальный исследовательский Томский политехнический университет ; под ред. А. Г. Фефеловой, Е. А. Покровской, И. О. Болотиной [и др.]. [С. 235-238].— , 2020 |
|---|---|
| Glavni avtor: | |
| Korporativna značnica: | |
| Drugi avtorji: | |
| Izvleček: | Заглавие с титульного экрана Misinterpretation of X-ray images can lead to a worsening of the patient's condition. The purpose of this research was to develop an algorithm for automatic annotation of diseases on the X-ray image in order to improve the accuracy of the analysis of medical images. This paper considers various methods for solving the classification problem. During the research a database of annotated medical images of radiography was compiled. On this basis, a compactly connected convolutional neural network was trained and tested. The classification accuracy of developed algorithm is above 66% for 14 classes of diseases. |
| Izdano: |
2020
|
| Teme: | |
| Online dostop: | http://earchive.tpu.ru/handle/11683/64713 |
| Format: | Elektronski Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=632673 |
| Izvleček: | Заглавие с титульного экрана Misinterpretation of X-ray images can lead to a worsening of the patient's condition. The purpose of this research was to develop an algorithm for automatic annotation of diseases on the X-ray image in order to improve the accuracy of the analysis of medical images. This paper considers various methods for solving the classification problem. During the research a database of annotated medical images of radiography was compiled. On this basis, a compactly connected convolutional neural network was trained and tested. The classification accuracy of developed algorithm is above 66% for 14 classes of diseases. |
|---|