# A Deep-Learning-Based Quality Control Evaluation Method for CT Phantom Images
## 4 Discussion  
CT has been a commonly used technique since the early days of medical examination. Because of their long-term use, older CT machines are in a state of serious deterioration. Nevertheless, if CTs are verified, aged equipment can continue to be used. However, testing with unverified equipment can lead to low-quality images, causing difficulties in the diagnosis of disease. This not only results in patients being exposed to increased doses of radiation but also leads to higher medical costs, thereby degrading the overall quality of medical services. To circumvent these issues, all medical equipment is subjected to regular quality checks to assess its condition [22]. In particular, for medical imaging equipment, standard phantoms are used for objective performance evaluation, and hence, most criteria have been quantified to facilitate objective assessment. However, the spatial and contrast resolution aspects of the phantom image tests are qualitative in nature and depend on human visual identification. Therefore, we aimed to apply artificial intelligence to evaluate CT phantom images. First, CT phantom images were acquired from various hospitals and machines. However, these images had limitations. Initial classification network training using available data achieved over 95% accuracy, indicating the feasibility of applying AI. However, the recall rate was excessively high during training, which was attributed to overfitting as a result of the data imbalance between the pass and non-pass images. To overcome this, non-pass images were created by consulting radiologists, radiology professors, practicing radiographers, and equipment accuracy control experts to establish the criteria for failing images. After applying various image-processing techniques, images that were deemed impossible to acquire clinically and those with conflicting opinions on pass/non-pass were removed. For the spatial resolution of images, Laplacian filters and dynamic histogram processing were adjusted to create passing images for different equipment. For the contrast resolution of images, failing images were created using image editing software based on the primary reasons for non-pass, including the inability to identify the fourth and fifth circles. Although this process reduced the total amount of data, it balanced the number of passing and failing data points for the re-experimentation. Assuming that the application of object area networks can enhance performance, we re-conducted labeling and training based on the YOLOv8 network, which is a commonly used object area network model. However, the accuracy was too low to replace qualitative evaluation. Therefore, two suitable attention blocks were selected. The internal C2f algorithms within the YOLOv8 network were modified for the application of these blocks. The accuracy was evaluated by changing the position of each block. Placing the CBAM block at the bottom of the backbone and the Swin block at the beginning yielded the best detection results. We believe that positioning the Swin block at the beginning of C2f emphasizes the features of small objects and that the CBAM structure, located at the end, effectively emphasizes the final extracted features. The proposed CTA-YOLOv8 network could be used as a basis for standardizing CT phantom image tests, which would quantify the objective performance of equipment, making it clinically valuable. Additionally, it could serve as evidence for assessing the performance of quantitative evaluation methods using AI for other medical equipment such as MRI or mammography devices, which also use phantoms for qualitative evaluation [23; 24]. In further research, more passing images are needed, and the one-class network can be applied as a new research direction. Moreover, we plan to develop a system to proactively establish standards for image acquisition in this area and to conduct follow-up research using additional data.