Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis
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 a...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/21/8189 |
| _version_ | 1797466522424180736 |
|---|---|
| author | Róża Dzierżak Zbigniew Omiotek |
| author_facet | Róża Dzierżak Zbigniew Omiotek |
| author_sort | Róża Dzierżak |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T18:40:59Z |
| format | Article |
| id | doaj.art-4c77cc9bd1db46c8a46856eae1f661d0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:40:59Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4c77cc9bd1db46c8a46856eae1f661d02023-11-24T06:44:05ZengMDPI AGSensors1424-82202022-10-012221818910.3390/s22218189Application of Deep Convolutional Neural Networks in the Diagnosis of OsteoporosisRóża Dzierżak0Zbigniew Omiotek1Department of Electronics and Information Technology, Lublin University of Technology, ul. Nadbystrzycka 38A, 20-618 Lublin, PolandDepartment of Electronics and Information Technology, Lublin University of Technology, ul. Nadbystrzycka 38A, 20-618 Lublin, PolandThe 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.https://www.mdpi.com/1424-8220/22/21/8189osteoporosisconvolutional neural networksdeep learningVGG16image classificationneural networks |
| spellingShingle | Róża Dzierżak Zbigniew Omiotek Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis Sensors osteoporosis convolutional neural networks deep learning VGG16 image classification neural networks |
| title | Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis |
| title_full | Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis |
| title_fullStr | Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis |
| title_full_unstemmed | Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis |
| title_short | Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis |
| title_sort | application of deep convolutional neural networks in the diagnosis of osteoporosis |
| topic | osteoporosis convolutional neural networks deep learning VGG16 image classification neural networks |
| url | https://www.mdpi.com/1424-8220/22/21/8189 |
| work_keys_str_mv | AT rozadzierzak applicationofdeepconvolutionalneuralnetworksinthediagnosisofosteoporosis AT zbigniewomiotek applicationofdeepconvolutionalneuralnetworksinthediagnosisofosteoporosis |