# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 4 Discussion  
In this study, we experimented and examined whether the application of a general HFV filter for vascular contrast enhancement and image fusion would improve the accuracy of deep learning-based stenosis detection in coronary angiographic images. This study included the training and analysis of preprocessed and postprocessed images using five deep learning networks. We demonstrated that image preprocessing based on HFV filters and image fusion can improve the processing efficiency of deep learning networks in complex coronary angiographic images and that this approach does not add much training complexity. It is worth noting that the images obtained by the HFV filter may appear to create false vascular-like structures or fail to enhance the real vessels. This could potentially lead to misclassification of the model during training and detection, resulting in a low mAP index. These findings raise the question of under what conditions the HFV filter can be accurately applied in preprocessing. Therefore, a rigorous evaluation of whether improvements in the visual appearance of images are made at the expense of image accuracy is needed. This is used as a starting point for further processing of the images. The number of features of interest in an image affects the performance of the trained model. To enable the model to better learn the stenosis features in coronary angiographic images, we introduced image fusion based on the use of HFV filters to enhance the vascular features in the images, especially the stenosis features with respect to fine vessels. Specifically, the image fusion technique complements the problem of feature loss after vessel enhancement and, to some extent, mitigates the problem of edge enhancement of small-sized features caused by the filter. The experimental results also demonstrate that the efficiency of stenosis detection for branching vessels can be enhanced to some extent using our proposed method. In our research, we sought to investigate the potential impact of the proposed methodology on feature learning. We took care to rigorously assess the degree of improvement in mean average precision (mAP) achieved through our approach, while also considering the possibility of dataset-related issues affecting our conclusions. To address this, we employed a \"chopping image\" technique to systematically evaluate the dataset's reliability. Our findings revealed an improvement in the performance of our neural network when applied to preprocessed images compared to their unprocessed counterparts. Concurrently, our research undertook a meticulous examination of the ramifications of different imaging angles on the precision of stenosis detection within coronary arteries. The empirical findings derived from our experiments unveiled a noteworthy revelation: the accuracy of stenosis detection exhibited conspicuous variations contingent upon the specific imaging angles scrutinized. Specifically, our investigations elucidated that the detection outcomes pertaining to the three angles corresponding to the left coronary artery (LCA) exhibited superior accuracy in comparison to those observed in the context of the right coronary artery (RCA). This intriguing and differentiating pattern in detection accuracy between these two arterial segments delineates a compelling avenue for further scholarly inquiry and investigation. It points to a potentially fertile area for future research endeavors aimed at optimizing the efficiency of coronary artery stenosis detection, offering invaluable options for the advancement of medical diagnostics and treatment strategies within this pivotal domain of healthcare. Our study is subject to several limitations that warrant discussion. These limitations encompass various facets of our research methodology and dataset, each contributing to a nuanced understanding of the constraints inherent in our investigation. Firstly, the composition of our dataset encompassed common angles of coronary angiography.