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The aim of this study was to assess the possibility of using deep convolutional neural
networks (DCNNs) to develop an effective method for diagnosing osteoporosis based on CT images of
the spine. The research material included the CT images of L1 spongy tissue belonging to 100 patients
(50 healthy and 50 diagnosed with osteoporosis). Six pre-trained DCNN architectures with different
topological depths (VGG16, VGG19, MobileNetV2, Xception, ResNet50, and InceptionResNetV2)
were used in the study. The best results were obtained for the VGG16 model characterised by the
lowest topological depth (ACC = 95%, TPR = 96%, and TNR = 94%). A specific challenge during
the study was the relatively small (for deep learning) number of observations (400 images). This
problem was solved using DCNN models pre-trained on a large dataset and a data augmentation
technique. The obtained results allow us to conclude that the transfer learning technique yields
satisfactory results during the construction of deep models for the diagnosis of osteoporosis based on
small datasets of CT images of the spine.
