Authors L. V. Utkin, A. A. Meldo, O. S. Ipatov, M. A. Ryabinin
Month, Year 08, 2018 @en
Index UDC 004.94
DOI 10.23683/2311-3103-2018-8-241-249
Abstract By taking into account a rapid development of new methods of artificial intelligence and a large number of new developments related to intelligent systems for diagnosing oncological diseases, the aim of the work is to consider the main peculiarities of such the systems and develop a perspective system architecture that allows us to increase the efficiency of their training process and the accuracy of the diagnostic results. The paper proposes a brief analysis of intelligent systems for diagnosing oncological diseases using an example of the lung cancer detection from computed tomography images, which are currently the main diagnostic tool for determining the prevalence of lung cancer, searching for local and distant metastases. The main types of existing intelligent diagnostic systems are considered and divided in subgroups from the point of view of the computed tomography information processing method usage. A description of the typical sequence of stages of the computed tomography image processing for detection of malignant tumors in the lung, which includes such procedures as the dataset collection, image pre-processing, segmentation, detection of lung nodules, reducing the number of false-positive cases and the classification of tumors. It is shown that the main problem of most differential diagnosis systems is a fact that the training sample contains few alternative examples of various types of cancer and cannot be fully used to train the intelligent diagnostics system. To solve this problem, a new architecture of the intelligent diagnostics system is proposed in the paper, which makes it possible to significantly increase the accuracy of the lung nodule classification at the last stages of data processing. The main basis of this architecture is the Siamese neural network, which consists of two identical subnets with shared parameters connected at the output. The neural network training process uses all possible pairs of samples from the image base of malignant tumors, which significantly increases the size of the training sample and eliminates the effect of overfitting. During testing the system, an analyzed computed tomography image as an example of an unknown tissue is fed to the input of one of the networks, and an image from the base of malignant tumors is fed to the input of the second network.

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Keywords Artificial intelligence; diagnostic system; lung cancer; computed tomography; neural network; image processing.
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