# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
## 4 Results and Discussion  
Diabetic retinal images are difficult to screen and identify in the early stages, and the patient's eyesight may deteriorate as the condition advances. A retinal model with three phases is presented in this manuscript. The retinal images are enhanced during the first step to reduce image noise. Then, segmentation of blood vessels is performed. The suggested model, which is based on the ResNet-101 architecture, is then trained using segmented images. The model is prepared to perform a similar task. The pool5 layer of the network extracts the 2048 features from each image and uses the dataset to train the fully connected layer. The weights adjust themselves and change dynamically in response to the output. The suggested approach correctly classified retinal pictures as normal, mild \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual**} \\  & **Positive: 0** & **Negative: 1** \\ \hline \multirow{2}{*}{Prediction} & Positive: 0 & True Positive & False Positive \\  & Negative: 1 & False Negative & True Negative \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Several metrics. diabetic, moderate diabetic, severe diabetic and hypertensive with a 98.72 percent accuracy. The authors can apply the proposed model to other medical datasets in the future. Evaluation of pre-processing is performed on MESSIDOR and ODIR (ocular disease intelligent recognition) datasets based on the PSNR ratio. The output of image enhancement on retinal images is shown in Figure 6. The comparison of the proposed pre-processing technique with the existing techniques is described in Figure 7.  
The enhanced image's PSNR value after applying adaptive CLAHE is about 35, which is significantly improved in comparison to prior methods. In addition, the DNCNN network is applied for enhancing the retinal images and reducing the noise. DNCNN is the valuable pre-trained model for image denoising. The PSNR value of the denoised images had improved significantly. After applying DNCNN, the PSNR value obtained is around 60, which is significantly higher than most existing approaches. Figure 7 depicts the performance of proposed enhancement methods with existing methods. Figure 8 illustrates the segmented images of blood vasculature. The set of numbers that are accurately predicted is referred to as the true positive (TP). The misclassified samples are represented by the false negative (FN) and false positive (FP) and the classified samples that are not predicted accurately are known as true negative (TN). Figure 6: Output of image enhancement in retinal images. Figure 7: Graph depicting the performance of pre-processing with other methods. Image enhancement is carried out to reduce noise. Many noises, such as salt and pepper and Gaussian noise, are frequently applied to the original photos. Image quality can be affected by noise, causing poor segmentation. To assure segmentation quality, pre-processing is performed using A-CLAHE, DNCNN and Wiener filters. DNCNN is a model that is pre-trained to remove noise in medical pictures. In the experiment, the PSNR value for the image with DNCNN produced higher results. Figure 9 depicts the analysis of blood vessel segmentation compared with existing methods based on sensitivity and specificity. Figure 8: Segmented images (original, enhanced, segmented image). The network is trained using the dataset, and the previous model's FC layer is replaced with the newly proposed connected layer model. In an 80:20 ratio, the network is tested and trained. To ensure unbiased sampling, the model uses random retinal images from the dataset. The confusion matrix of the dataset MESSIDOR and ODIR is depicted in Figure 10. In comparison to earlier methods, the proposed model has a 98.72 percent accuracy, which is quite high and ultimately accurate in all scenarios, as illustrated in Figures 11 and 12. Figure 13 depicts how the proposed method improves existing state-of-the-art models in an accuracy comparison analysis. Figure 10: Confusion matrix of MESSIDOR and ODIR datasets.