# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 5 Results and Discussion
### Bone Classification Using a Convolutional Neural Network: Case Study Examples  
In this experiment, a deep learning model named the convolutional neural network is used to classify between normal bone and osteopororotic bone. The experiment is carried out in two scenarios; in the first scenario, the datasets used to train the models are from CT scan images of normal and osteopororotic bone only. Meanwhile, for the second scenario, the attributes of roughness index, phase index, and contrast attributes were included as the input together with the original CT scan image for both normal and osteopororotic bone. The datasets are shuffled and normalized to help better train the model. To avoid an imbalanced dataset during the training of the model, the datasets selected have the same number of normal and osteopororotic bones. The total amount of data used during the training stage is 50 images, which consist of the same number normal and osteoporosis bone images. In the testing stages, ten images consisting of five samples of normal and osteopororotic bone were used. In the CT images used for this experiment, 3D slicing with axial slicing was selected. Examples of images taken for the training and testing stages are shown in Figure 8.  
The model is trained and tested in different patients to avoid bias and to make the model more objective. Blind testing of this model was carried out with different patients, with the five images taken from each normal and osteoporoprotein bone. The results are shown in Figure 10.  
Figure 10: Classification results of **(a)** Scenario 1: CT scan only, and **(b)** CT scan and its attributes together with the confusion matrix, green color indicate the same class while red indicate different class. Figure 9: Accuracy, loss function, and confusion matrix plot of **(a)** Scenario 1: input CT scan only, and **(b)** Scenario 2: input including CT scan and its attribute, green color indicate the same class while red indicate different class. Additional input into the CNN engine through adding the attributes to the image layer increased the performance of the engine. Scenario 1, in which the input is only a CT scan image, failed to classify one osteoporotic bone image; however, all normal bones were predicted well (90% were well predicted). Scenario 2, in which the input consisted of CT scan images including its attribute, successfully classified the image testing (100% were well predicted). The ablation study for the proposed five indices has been conducted to evaluate the individual contribution of each attribute as an additional feature to complement the original images. The results show that the roughness index gives the highest performance, followed by the contrast attribute and phase. However, the frequency index does not show a positive contribution to the performance. The performance of the contribution is calculated as the accuracy and loss plot versus iteration, and the confusion matrix as shown in Appendix A. The experiments were conducted on 2D images selected from 3D CT scan images. Because the slicing is chosen on the axial view, the distribution of bone properties in the sagittal and coronal view is not considered. The experiments would be more precise if the bone was evaluated as a 3D object, whereby the analysis is voxel-based rather than pixel-based. Voxel embedded-based deep learning has successfully been applied to 3D instance segmentation in a quantifying dynamic cellular case study [16]. Osteoporosis will impact the quantitative and qualitative measurements of bone, as the bone mass becomes lesser and more fragile. If the attributes are extended into 3D images, they may better represent the bone quality. A combined approach with embedded-based deep learning may be an alternative for future work on osteoporosis identification. Another limitation in identifying osteoporosis based on a CT scan is the segmentation of the bone itself; the bone should be segmented from other organ bodies.