Anna A. Meldo
Lev V. Utkin
Mikhail A. Ryabinin
Received 24 April 2019
The paper deals with a problem of automating and intellectualizing a diagnostic study of lung cancer based on computed tomography scans. The relevance of the developed system is based on the fact that lung cancer in many countries is the most common cancer and its detection at an early stage may significantly increase the chance of patient survival. The use of automated diagnostic systems partially solves the problem of diagnosis at an early stage. The article proposes a new architecture of a part of an automated system, the purpose of which is segmentation and detection of nodules in lungs and minimization of missed nodules, which determines the sensitivity of the system. Its peculiarity lies in the fact that, along with deep learning algorithms based on the use of deep neural networks (2D U-Net and 3D U-Net), traditional computed tomography image processing algorithms are used in parallel, which increase the system's ability to detect tumors. The system is a three-channel data processing system, where each channel is a procedure for detecting nodules. The training process of the system is based on the use of an open labelled database of computed tomography scans of patients called LU-NA16 and a new database containing atypical cases of lung cancer called LIRA. The implementation of the preprocessing procedure and of the lung segmentation procedure are considered. Architectures of deep neural networks are proposed which are the most suitable for detecting nodules. Segmented nodules of a new patient can be viewed as the system output.
Deep neural network, image processing, image segmentation, lung cancer, artificial intelligence, computed tomography.
This work is supported by the Russian Science Foundation under grant 18-11-00078.
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