# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 3 Experiments and Results
### Results
Figure 4(b1) through Figure 4(b6) vividly illustrates the outcomes of this image fusion technique applied to the processed images across the six imaging angles. Through image fusion, we effectively retained the salient features present in the original images, thereby ensuring their preservation. Concurrently, the noise and random morphological features inherent in the original images, which had the potential to transform into vascular-like structures due to the HFV filter application, were substantially mitigated. After determining that the vascular structures in the image were enhanced, we again introduced the neural network applied to the original image and examined its performance for detection under the same conditions. Figure 5 shows the performance of these neural networks on preprocessed images. Figure 3: Example of stenosis detection by applying YOLOv4 on the dataset without HFV filter: the yellow bounding boxes respect the stenosis location detected by the trained detector and the red bounding boxes illustrate the annotation. Similar to the previous results, YOLOv4 still had the lowest accuracy among the five networks, although the accuracy had improved. The mAP values were 0.3785 and 0.2524 at IoU values of 0.1 and 0.6, respectively. The R-FCN-Inceptionresnetv2 network remained the optimal model with mAP values of 0.6341 and 0.5055 at IoU values of 0.1 and 0.6, respectively. By observing the last row of the table, it can be noticed that the preprocessing method has improved the accuracy of the model. For YOLOv4, the detection accuracy was improved by 118-135%. Meanwhile, for R-FCN-Inceptionresnetv2, which was the best network, the detection accuracy was improved by 112-116%. Nevertheless, it is crucial to address certain challenges posed by specific imaging angles, as exemplified in Figure 1(b2), which pertains to the left anterior oblique (LAO), right anterior oblique (RAO), and ZERO angles within the right coronary artery (RCA). Figure 4: Stenosis detection cases of the model from six camera angles. Among them, (**a1**–**a6**) represent the detection results using the R-FCN-Inceptionresnetv2 network under the conditions of left coronary artery (LCA) zero, LCA left anterior oblique (LAO), LCA right anterior oblique (RAO), right coronary artery (RCA) zero, RCA LAO, and RCA RAO, respectively. (**b1**–**b6**) corresponds to the stenosis detection situation of (**a1**–**a6**) in the case of preprocessing by HFV filter and image fusion. Figure 5: Network performance on original dataset and dataset preprocessed by HFV filter and image fusion. In these cases, complexities such as vessel overlap arising from the camera angle can significantly impede the model's ability to accurately detect the precise location of stenosis. In pursuit of a more intuitive assessment of the potential enhancement conferred by the preprocessing approach and the model's performance on our dataset, we adopted an image cropping strategy. This strategy involved the extraction of the central portion of the original image, characterized by a concentrated distribution of blood vessels, to form a refined dataset for network training. The primary objective of this approach was twofold: firstly, to mitigate the influence of noise within the dataset and, secondly, to minimize the impact of misclassifications resulting from the uneven distribution of the contrast agent. The outcomes of these experiments are depicted in Figure 6.  
In Figure 6, the performance of both YOLOv4 and R-FCN-Inseptionresnetv2, with different IoU values, had obviously improved. R-FCN-Inseptionresnetv2 obtained the mAP value of 0.7551 in the preprocessed dataset with an IoU value of 0.5. Regarding the magnitude of the improvement, for the cropped images, the preprocessed images showed a 102-111% improvement in the mAP values compared with the original images. Furthermore, to comprehensively evaluate the performance of our approach from multiple imaging perspectives, we conducted a meticulous analysis for each imaging angle within our test dataset.