Three-channel intelligent neoplasm classification system for the diagnosis of lung cancer

Three-channel intelligent neoplasm classification system for the diagnosis of lung cancer

Lev V. Utkin *
Doctor of Technical Science, Professor, Peter the Great Saint-Petersburg Polytechnical University (SPbPU), Research Laboratory of Neural Network Technologies and Artificial Intelligence, Head of Laboratory, Head of the Telematics Department, 29, Politekhnich-eskaya ul., Saint-Petersburg, 195251, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0002-5637-1420

Anna A. Meldo
PhD in Medical Sciences, Saint-Petersburg Clinical Research Center of Specialized Types of Medical Care (Oncological), Head of the Radiology Department, Radiologyst, 68A, Leningradskaja ul., Pesochny, Leningrad region, 197758, Russia; SPbPU, Research Laboratory of Neural Network Technologies and Artificial Intelligence, Senior Research Scientist, 29, Politekhnicheskaya ul., Saint-Petersburg, 195251, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it. 

Viktor S. Kryshtapovich
SPbPU, Bachelor's Degree in Mathematics and Computer Sciences, 29, Politekhnicheskaya ul., Saint-Petersburg, 195251, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it. 

Viktor A. Tiulpin
SPbPU, Bachelor's Degree in Mathematics and Computer Science, 29, Politekhnicheskaya ul., Saint-Petersburg, 195251, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0002-2005-436X

Ernest M. Kasimov 
SPbPU, Master's Degree in Mathematics and Computer Sciences, 29, Politekhnicheskaya ul., Saint-Petersburg, 195251, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it. 

Maxim S. Kovalev
SPbPU, Master's Degree in Mathematics and Computer Sciences, Research Laboratory of Neural Network Technologies and Artificial Intelligence, Engineer, 29, Politekhnicheskaya ul., Saint-Petersburg, 195251, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it. 

Received 15 July 2019

Abstract
A task of the efficient classification system construction for segmented lung nodules as the most important element of an intelligent system for the lung cancer diagnosing based on computed tomography scans is studied. The system is trained on open data sets for the lung cancer diagnosis and solves the problem of classifying the lung tumors detected by a segmentation system, and the problem of reducing the number of false-positive cases. Results of its functioning are the probabilities of various diagnoses. The classification system is based on the following approaches. First, segmented nodules are represented in the form of five histograms that characterize the internal structure of nodules, their surface and their surroundings, which allow us to significantly reduce the data dimensionality and to simplify the classification process. Secondly, Siamese neural networks are used for classification, which are an efficient tool that allows us to carry out the differential diagnosis of oncological diseases. Third, a three-channel classification system is proposed, where the first channel uses a cascade of random forests to classify histograms, the second channel is a Siamese neural network consisting of simple neural networks trained in pairs of histograms, and the third channel uses a Siamese neural consisting of convolutional neural networks trained on pairs of segmented 3D objects from the computed tomography images. The resulting diagnosis is determined on the basis of a combination of the probabilities of the diagnoses obtained in each channel and on the basis of the classification accuracy computed by using testing examples.

Key words
Deep neural network, classification, radiomics, lung cancer, artificial intelligence, computed tomography, Siamese neural network.

Acknowledgements
This work is supported by the Russian Science Foundation under grant 18-11-00078.

DOI

https://doi.org/10.31776/RTCJ.7304 

Bibliographic description
Utkin, L. et al. (2019). Three-channel intelligent neoplasm classification system for the diagnosis of lung cancer. Robotics and Technical Cybernetics, 7(3), pp.196-207.

UDC identifier:
004.896:616.24

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