A results subsection has been added on pages 5 to 8, which explains the applicability of the results of this study and how other researchers can use these results. This article proposes a new model for the classification of hypertensive retinopathy using constrained Boltzmann machines.
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Restricted Boltzmann Machines for Fundus Image Reconstruction and Classification of Hypertension
Introduction
In addition, each visible unit is connected to all hidden units represented by a series of weights, so that each hidden unit is also connected to all visible units and bias units connected to all visible units represents the number of hidden neurons. In general, the goal of the RBM algorithm is to reconstruct the input as accurately as possible. Then the input is changed based on weight and bias and then used to convert the input into an output.
At this stage, the input layer tries to change the activation as an input reconstruction and then uses this input to compare with the original input (Ranzato et al., 2010). In the case of computer vision, each visible unit corresponds to a pixel value from the image, while the hidden units represent independent specific features of the image. The weights connecting the visible and the hidden units are usually trained using contrastive divergence learning, which is an approximation of maximum likelihood learning (Xia et al., 2016).
Methods using RBM have become more popular in recent years, and they have been successfully applied to image recognition (Yamashita et al., 2014).
Related Work
2. probabilistic elements) which consists of two binary entities namely visible layer is stated to be observed and the hidden layer is feature detectors and unit bias. The detection of hypertensive retinopathy using the neural network has also been proposed by Syahputra et al., (2018) and Arsalan et al., (2019). Syahputra et al., used the Backpropagation Neural Network model and the STARE dataset with 95% accuracy, while Arsalan et al., used the Vess-Net model and three datasets DRIVE, CHASE-DB1 and STARE with an accuracy of
Material And Methods
We calculated the arterial to venous width ratio (AVR) from 89 retinal image samples by Hubbard et al. 2013) methods, The following is to segment retinal blood vessels, measure AVR and label retinal images into nine classes based on AVR for training model by modifying the category of HR by Abbasi and Akram. 1) (2) where v is the visible layer, h is the hidden layer, D is the number of visible units, and P is the number of hidden units, as well as training datasets in vectors N. 3) The RBM model input in the form of a retinal color image, each value of the intensity of the image pixel is read and converted to a value between 0 to 1, and then becomes the input for visible nodes, so the number of visible nodes corresponds to the number of pixels of the input image. Then the first iteration process is to adjust the connection weights between each visible node and each hidden node until we get the output of hidden nodes which then updates the value of the visible node.
While testing is the testing phase of the model that was performed in the training phase. In this testing phase, the test set of data from the MESSIDOR database was used, where the 30 samples of the test set of data were not used in the model training process. The number of epochs is 20, the batch size is 30, and the number of sample images for testing is 30 randomly selected.
The learning rate of the four models is 0.05 and the hidden number of the four models is the same as 1500 units.
Result
The number of training datasets is 1200 retinal images with dimensions of 64x64x3 pixels, 40% of the data or 480 images are used for validation and a sample of 30 images is used for testing the RBM model. Third, the more the number of hidden nodes, the less the accuracy of RBM model testing, the difference is. 5 shows a graph of the training results of four RBM models with different number of hidden nodes.
This empirically proves that the learning rate of the ideal RBM model is greater than 0.005. 6 shows a graph of the training results of the four RBM models with the number of hidden nodes varying. Based on the four graphs of the experimental results, it can be concluded that the RBM model with a learning rate of 0.5 and 0.05 has a relatively similar trend in performance levels of training errors and validation.
Among the three experimental scenarios and the empirical data of the experimental results, the RBM model with the number of hidden nodes 1500 and learning rate 0.05 is the RBM model with the best performance.
Discussion
In the model with a learning rate value of 0.005, there are fluctuations in the training and validation error rates from epochs 7 to 11, although at the end of the 20th epoch, the three types of RBM models above have relatively similar training levels and validation errors below 2%. When analyzing and discussing the experimental results of the retinopathy and hypertension classification model using RBM, it was found that, firstly, the model was able to reconstruct the input image into one of the image classes with a relatively small error rate. The model we propose based on the results of this study is still very much open to further development.
The usefulness of this study results are: First, a new set of data for the classification of hypertensive retinopathy into nine classes, which can be used as a standard database for other researchers to test their proposed model . Second, the RBM classification model can be applied to the classification of retinal images experiencing noise because the RBM model is capable of reconstructing images. Fourth, the model we propose can be developed as a tool for ophthalmologists to aid the diagnosis and early detection of hypertensive retinopathy, based on the image of the patient's retina.
Conclusion
New method for automated analysis of retinal images: results in subjects with hypertensive retinopathy and CADASIL. Abstract: Conventional classification of hypertensive retinopathy through the analysis of fundus images by experts, but this method results are highly dependent on the accuracy of observations and expert experience. In this study, we propose a fundus image reconstruction and hypertensive retinopathy classification model using Restricted Boltzmann Machines (RBM), as well as the Messidor database that has been labeled as a dataset.
Previous studies regarding the classification of hypertensive retinopathy used features of AVR with datasets DRIVE and VICAVR (Khitran et al., 2014). They used 100 images of hypertensive retinopathy patients and used four methods, Artificial Neural Networks (ANN), Naive Bayes, Decision Tree (DT), Support Vector Machine (SVM) with an accuracy of and 81%, respectively. While (Akbar et al., 2018) proposed the detection of hypertensive retinopathy using edge detection of arterial and venous vessels on retinal images from three datasets of INSPIRE-AVR, VICAVR and AVRDB, with 95, 96.8 and 98.8% respectively .
The detection of hypertensive retinopathy using the neural network has also been proposed by (Syahputra et al., 2018; Arsalan et al., 2019).
158 Materials and Methods
In this study, we used RBM to classify hypertensive retinopathy based on retinal images. The input of the RBM model in the form of a color retinal image, each pixel intensity value is read and converted to a value between 0 and 1, then it becomes the input for visible nodes, so that the number of visible nodes corresponds to the number of pixels of the input image. The dataset consists of nine classes of hypertensive retinopathy that are categorized and annotated.
This research was also implemented four types of RBM models with a different number of hidden layers, each of and 2000 units.
Results
Third, the more number of hidden nodes, the less test accuracy of the RBM model, the difference is very small or not too significant. From the four graphs, it appears that the performance of the RBM model is almost the same as the results of the model trials using variations in the number of visible nodes, where at the beginning of epoch 0 to 5 the validation error rate is relatively lower than the training error level , it shows that there is overfitting, but after the fifth epoch, showing that error and validation training had the same trend until the 19th epoch. Finally, the convergence of the training error training rate and the validation error rate of the four types of RBM models occurred after the tenth epoch.
The number of training datasets is 1200 retinal color images with dimensions 6464 pixels, 40% of the data or 480 images are used for validation and a sample of 30 images is used for testing the RBM model. The analysis and discussion of the experimental results of the Hypertensive Retinopathy Classification Model using RBM was completed, first, the model could reconstruct the input image into one of the image classes with a relatively small error rate. Third, the model we propose can be applied to the classification of other medical images such as prostate, lung, and other images.
Second, the experimental results of the Retinopathy Hypertension Classification Model using RBM prove that the model can reconstruct the input image into one of the image classes with a relatively small error rate.
165 Acknowledgment
Author’s Contributions
Ethics
