# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 4 Discussion  
This paper presents a three-stage technique to detect hemorrhages in retinal fundus images. The proposed method can perform effectively with images obtained under different illumination conditions. Regardless of size and location, the proposed method effectively detects hemorrhage attached to a blood vessel (see Figures 6 and 7). Separate estimations were performed for precision, recall, and F1 scores for both datasets. The results of precision, recall, and F1 scores for the proposed method produced higher values for the DIARETDB1 dataset when compared with results from DIARETDB0. For DIARETDB1, the results of precision, recall, and F1 score were estimated to be 83.97%, 83.74%, and 83.85%. For DIARETDB0, the results decreased to 70.51%, 74.08%, and 72.25%. A major reason for the decrease could be the quality of images. The proposed method also offers a high level of preprocessing (contrast and enhancement) which gives the segmentation algorithm higher visual and quantitative results. While the state-of-the-art segmentation techniques perform well, their results are not satisfactory. For example, KMC, RG, ACC-V, ACMS, and FCLS produce F1 scores of 62.46, 63.53, 72.99, 67.45, and 78.23, respectively. Specifically, KMC and RG produced the worse performance, while ACMS and FCLS produced the best results among competing methods. Overall, the proposed method performs better than all the methods. Figures 9 and 10 depict the statistical comparison of all methods.  
Figure 8: Results of pre-processing algorithm (**a**) original image, (**b**) HE, (**c**) AGCWD, (**d**) BPDFHE, (**e**) NMHE, (**f**) GAGC.  
Comparison of methods with the DIARETDB1 dataset (Figure 9) shows that KMC produces a good result for recall; however, results for precision and F1 score are not satisfactory. The RG algorithm produces a good result for precision, but results for recall and F1 score are not satisfactory. In addition, ACMS, ACC-V, and FCLS produce average results for all evaluation metrics. Overall, the proposed method produces a good result for all evaluation metrics. In the DIARETDB0 dataset (Figure 10), all methods closely compete, especially with the precision evaluation metrics. However, for recall and F1 score, the proposed method, KMC, ACC-V, and FCLS algorithm, perform well. The reason for good performance by the proposed method is because of the SWAT technique and the efficient preprocessing procedure.  
Figure 11 shows the plot of the curve between the true positive (TP) rate and the false positive (FP) rate for different probability thresholds. A higher area under the curve (AUC) value shows a higher capability of the classifier. The behavior of the classifier changes significantly by changing the kernel function. The selection of suitable kernel function is very important and is carried out by analyzing the ROC curves of the linear, radial basis function (RBF), and polynomial kernels. The AUC of RBF is the largest; therefore, we use it to classify hemorrhages. By visual comparison, cases with extreme difficulty further demonstrate that the proposed method produces accurate segmentation results. In the two  
Figure 10: Statistical comparison of segmentation methods (DIARETDB0 dataset).  
Figure 9: Statistical comparison of segmentation methods (DIARETDB1 dataset).  
datasets, when the proposed method was compared with other competing methods, we discover that the proposed method produces an acceptable result despite some missing targets.  
We tried to test our research on a dataset collected from the local hospital. However, the availability and copyright law were the major delimitations during our experiment.