Segmentation of Tuberculosis Lungs on Computer Tomography Images
DOI:
https://doi.org/10.20535/ibb.2021.5.2.233051Keywords:
pathology segmentation, neural network, tuberculosis, U-Net, artificial intelligence, training networksAbstract
Background. Tuberculosis is a chronic lung disease that occurs due to a bacterial infection and is one of the top ten causes of human death. As part of the automated diagnostic system, the detecting tuberculosis lesions on computed tomograms of the lungs in automatic mode is an urgent task.
Objective. We are aimed to solve the lungs segmentation tuberculosis-affected areas problem on computer tomograms using digital image processing based on U-networks.
Methods. The data for training the network were provided by the specialists of National Institute of Phthisiology and Pulmonology named after F.V. Yanovsky, NAMS of Ukraine. We performed the image segmentation by applying artificial intelligence using the convolutional neural network UNet, which has been developed for medical segmentation tasks. We considered three versions of UNet networks with different parameter values. A feature of U-Net is the absence of fully connected layers. This network is an example of an encoder-decoder architecture, which shows high results in problems of semantic image segmentation. In the last two models, we applied the technique of early stopping of training which avoids the effect of overfitting the network. The number of training epochs is set with a margin, and the process of training network parameters stops as soon as the model performance stops improving on the test data set.
Results. The data set was divided into 320 samples (80%) for training, 40 samples (10%) for testing, and 40 samples (10%) for the exam. The effectiveness of the developed models was evaluated by the parameters: Precision, Recall, and Matthews correlation coefficient. The final model provides high performance on the exam, such as accuracy of 0.82, sensitivity of 0.75, Matthews correlation coefficient of 78%.
Conclusions. The conducted studies using the UNet network allowed us to obtain high results for the segmentation of tuberculosis lesions on computed tomography images. The proposed network will be used in the further development of diagnostic systems for tuberculosis.
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