Identification of Pathological Formations in the Lungs Based on Machine Learning Methods
- 10.2991/aisr.k.201029.060How to use a DOI?
- machine learning, neural networks, CNN, computer tomography, lungs’ pathological formations
The article discusses the use of deep neural networks for analysis and recognition of images of computed lung tomography. The recognition is carried out in two stages: segmentation of lung image on the CT slice and search of pathological entity. An algorithm for segmentation of lung images in images is proposed and a model for finding pathological formations based on machine learning methods is developed. To implement the stage of recognition of pathological formations, CNN convolutional neural network is used. The developed approach ensures that areas containing pathological formations are found on sections of pictures. Images from the public LIDC/IDRI database were used to test the model. The efficiency analysis showed on the test data the accuracy of the proposed model 0.82. The software is implemented in the programming language Python 3.6 and is cross-platform. For machine learning algorithms TensorFlow 1.14, Scikit-learn 0.22.1 packages were used.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - G. R. Shakhmametova AU - N.O. Vakkazov AU - R.Kh. Zulkarneev PY - 2020 DA - 2020/11/10 TI - Identification of Pathological Formations in the Lungs Based on Machine Learning Methods BT - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020) PB - Atlantis Press SP - 318 EP - 322 SN - 1951-6851 UR - https://doi.org/10.2991/aisr.k.201029.060 DO - 10.2991/aisr.k.201029.060 ID - Shakhmametova2020 ER -