Examining the Validity of Input Lung CT Images Submitted to the AI-Based Computerized Diagnosis

Aleksandra A. Kosareva (Login required)
Belarusian State University of Informatics and Radioelectronics, Minsk, Belarus
The United Institute of Informatics Problems of NAS of Belarus (UIIP NASB), Biomedical Image Analysis Department, Minsk, Belarus

Dzmitry A. Paulenka
The United Institute of Informatics Problems of NAS of Belarus (UIIP NASB), Biomedical Image Analysis Department, Minsk, Belarus

Eduard V. Snezhko
The United Institute of Informatics Problems of NAS of Belarus (UIIP NASB), Biomedical Image Analysis Department, Minsk, Belarus

Ivan A. Bratchenko
Samara University, Department of Laser and Biotechnical Systems, Samara, Russia

Vassili A. Kovalev
The United Institute of Informatics Problems of NAS of Belarus (UIIP NASB), Biomedical Image Analysis Department, Minsk, Belarus


Paper #3500 received 23 Jun 2022; revised manuscript received 05 Sep 2022; accepted for publication 16 Sep 2022; published online 30 Sep 2022.

DOI: 10.18287/JBPE22.08.030307

Abstract

A well-designed CAD tool should respond to input requests, user actions, and perform input checks. Thus, an important element of such a tool is the pre-processing of incoming data and screening out those data that cannot be processed by the application. In this paper, we consider non-trivial methods of chest computed tomography (CT) images verifications: modality and human chest checks. We review sources to develop training datasets, describe architectures of convolution neural networks (CNN), clarify pre-processing and augmentation processes of chest CT scans and show results of training. The developed application showed good results: 100% classification accuracy on the test dataset for modality check and 89% classification accuracy on the test dataset for checking of lungs presence. Analysis of wrong predictions showed that the model performs poorly on biopsy of lungs. In general, the developed input data validation model shows good results on the designed datasets for CT image modality check and for checking of lungs presence.

Keywords

image classification; medical imaging; convolutional neural network; deep learning; computer-aided diagnosis; computed tomography; input validation

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References


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