# Efficient Cervical Cell Lesion Recognition Method Based on Dual Path Network

Authors: Mingwei Qin; Yongxiang Gao; Haifeng Sun; Mingliang Gou; Ning Zhou; Yuancheng Yao
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

Ubiquitous computing and artificial intelligence are widely used in the field of Wise Information Technology of Med. It promotes the development of intelligent diagnosis and treatment, as well as reducing the workload of doctors’ diagnosis and promoting the improvement of the diagnosis level of medical institutions in remote areas. The accurate detection of cervical cytopathy is related to the precise treatment and rehabilitation of patients. However, the low rate of accuracy in the existing cervical cytopathy image detection cannot satisfy the application needs of intelligent diagnosis. In this paper, a dual path network efficient detection method for cervical cytopathy (the proposed DSRNet50) is proposed, which is based on the deep learning method, and combines residual structure and dense connection. The proposed DSRNet50 is mainly based on residual structure, supplemented by dense connection paths, which improves the utilization of features, and maintains the ability of exploring new features by reducing feature redundancy. Meanwhile, the proposed DSRNet50 leverages packet convolution to reduce the computation burden of the network and eliminate the over fitting phenomenon of the network. The proposed DSRNet50 further exploits channel attention mechanism to recalibrate important features to suppress the propagation of irrelevant information. We use the Herlev dataset to verify the proposed DSRNet50 in the terms of the detection accuracy, parameter quantity, and computing complexity. The experiment results are presented to show the achievable performance of the proposed DSRNet50.


---

## Body

## 1. Introduction

In recent years, ubiquitous computing and artificial intelligence technology have developed rapidly and have been widely used in the medical field [1], the automobile field [2], and the UAV communication field [3]. In the medical field, with the development of Wise Information Technology of Med (WITMED), people can obtain digital medical services anytime and anywhere in an environment full of computing and communication capabilities. The intelligent diagnosis method based on medical knowledge, combined with computer technology, image processing technology, and artificial intelligence, as the key technology in WITMED, can automatically identify suspicious lesions, improve doctors’ work efficiency, reduce the misdiagnosis rate and the missed diagnosis rate, improve the diagnosis and treatment level, and solve the problem of lack of medical resources in remote areas. As the most common and effective means of early diagnosis and prevention of cervical cancer and its precancerous lesions [4], cervical cell screening plays an important role in the diagnosis and prevention of cervical cell lesions. Before the emergence of intelligent diagnosis systems, the main method used was manual film reading. Manual film reading has the disadvantages of long diagnosis time, low efficiency [5], and high dependence of accuracy on the level of professional technicians. The sensitivity of film readers is only about 65% due to fatigue, skill level, and subjective interpretation [6], which is difficult to meet the needs of cervical cancer screening in the current society. With the emergence of intelligent diagnosis technology, especially in cell classification, intelligent diagnosis technology has become a research hotspot of WITMED. Because it adds the experience and knowledge of highly skilled doctors, and it has the characteristics of high film reading efficiency and not affected by working hours, doctors in remote areas can also rely on the intelligent diagnosis system to treat difficult and miscellaneous diseases. Therefore, intelligent diagnosis technology has become a research hotspot of WITMED.In order to meet the requirements of intelligent diagnosis, it is necessary to further improve the accuracy of cervical cytopathy detection. At the same time, in order to meet the low-cost characteristics of ubiquitous computing, it is necessary to reduce the increased computing resources as much as possible. Based on the above requirements, this paper proposed DSRNet50 based on dual path network, which takes the residual structure as the main body, adds grouping convolution to reduce the amount of parameters, and then uses the channel attention mechanism to help the network learn important feature information, suppress unimportant feature information, realize feature recalibration, and use dense connection to build a dual path structure, so that the network can continue to explore new features. It not only increases very few computing resources but also greatly increases the accuracy and meets the needs of intelligent diagnosis and ubiquitous computing.The sections of this paper are mainly arranged as follows:The first section introduces the development and advantages of pervasive computing and artificial intelligence technology in the medical field, especially the importance of cervical cell reading in cervical cancer diagnosis, illustrates the limitations of traditional reading methods with examples, and then introduces the advantages of intelligent diagnosis and treatment technology, which has the problem of insufficient accuracy in cervical cancer cell reading. Therefore, the DSRNet50 network proposed in this paper is introduced. It can effectively improve the accuracy of film reading and introduces the contribution of this workThe second section introduces the related work of cervical cytopathy recognition and compares it with the work of this paperThe third section introduces the DSRNet50 proposed in this paper. The principle and structure of DSRNet50 are introduced in detailThe fourth section introduces the experimental and simulation results, including dataset selection, dataset preprocessing, evaluation parameter analysis, and simulation results. The results show that the network proposed in this paper has better recognition effect of cervical cytopathy than other networks and only needs to increase a small amount of calculationThe fifth section summarizes and prospects the article


## 2. Related Works

As an algorithm applied in intelligent diagnosis technology, cervical cell image classification algorithms can assist doctors to complete cervical cell screening. In the early cervical cell image classification algorithm, the morphology and texture of cells were mainly selected manually, such as cell shapes, areas, dyeing depths, the roundness of nucleus, and the nucleocytoplasmic ratio [7], and then a specific classifier or a neural network was used for automatic classification. However, this method is limited by the labeling accuracy and the segmentation accuracy of cell image features, and its accuracy fluctuates greatly; so, it is not enough to be included in the primary screening method [8]. Although the cell segmentation algorithm has been optimized [9–13], the similarity between cell clusters and normal/abnormal cells still hinders the accurate segmentation of cells. Once the segmentation deviates [11], the classification accuracy will decline. For example, Lassouaoui and Hamami. used the region growth algorithm for cervical cell image segmentation [14]. Plissiti and Nikou extracted the features of the nucleus, used the principal component analysis algorithm to reduce the dimension, and then used the fuzzy mean algorithm as the classifier [15]. Genctav et al. and Nayar and Wilbur grouped and sorted according to TBS (cervical cell standard classification system) [16, 17] and used the hierarchical clustering algorithm to construct decision trees for classification. Zhao et al. used a three-stage strategy to classify cervical cancerous cells [18], extracted 120 Witt’s sign, and used it for the training of linear classifiers.As a deep learning model, the convolution neural network convolutes the input data, reduces the complexity of data, automatically obtains image features, avoids manual design features, and implicitly extracts image features, which just makes up for the low accuracy caused by large data deviation. In 2012, Krizhevsky et al. applied the deep convolution neural network model AlexNet to ImageNet dataset [19], which improved its accuracy by about 10%. Zhang et al. applied convolutional neural network to the classification task of normal and abnormal cervical cells for the first time [20] and migrated the model in the ImageNet large-scale visual recognition challenge to the classification model of cervical cell image but did not make detailed classification according to the lesion level. Since then, many studies on cervical cell classification have emerged, such as Xu et al.’s image of cervical dysplasia combined with multimodal data and deep learning for cancer diagnosis [21]. Sarwar et al. established a standard database based on the cell images and cervical cytopathological knowledge and used convolutional neural network to realize the digitization of screening [22]. The above methods can realize cancer recognition. In addition, Tareef and others have also achieved good results in the recognition of overlapping cell images by using dynamic shape modeling method combined with deep learning [23]. Although the above methods have improved the accuracy to a certain extent, there are still deficiencies in sensitivity and recall. To be applied to intelligent diagnosis, the indicators need to be further improved.The network proposed in this paper focuses on the defects of low sensitivity and recall rate in the above research and takes into account the problem of computing resources for actual deployment. The follow-up experimental results show that the indicators are significantly improved than the previous literature and can effectively identify cervical cytopathy.


## 3. The Proposed DSRNet50

The proposed DSRNet50 introduced residual network, block convolution, dense connection path, and channel attention mechanism. This section will first introduce the principle of each part of the proposed DSRNet50, briefly describe the reason why it is added to the proposed DSRNet50, and then introduce the overall network structure of the proposed DSRNet50.


### 3.1. Principle of DSRNet50

#### 3.1.1. The First Component of DSRNet50: ResNet50

For deep neural networks, it is generally believed that the deeper the number of network layers, the stronger the nonlinear expression ability of the network, and the network can learn more features; so, the accuracy should be higher. However, it is found that this is not the case in the experimental process. With the increase of the number of layers, the training effect of the traditional convolutional neural network will become worse, and the accuracy will become lower. In order to solve this problem, in 2015, He and others proposed the deep residual network ResNet50 [24]. ResNet50 is a deep residual network which has 50 network layers.The design idea of ResNet50 is as follows: assuming that a networkA can work well, and now there is a network B which is deeper than A, let the former part of B be exactly the same as A, so that the latter part only needs to realize an identity mapping, so that B can at least obtain the same performance as A. As shown in Figure 1, assuming that the input of A certain neural network is x and the expected output is Hx, i.e., Hx is the expected complex potential mapping, and it will be more difficult to learn such a model.Figure 1
Structural diagram of residual block.Back to the previous hypothesis, when the learning accuracy is saturated, or when the error of the lower layer is found to be larger, the next learning goal can be transformed into the learning of identity mapping, even if the inputx is similar to the output Hx, so that the accuracy of the network will not decline in the later level.In the residual network structure diagram shown in Figure1, the input x is directly transmitted to the output as the initial result through “shortcut connections,” and the output is Hx=Fx+x. if Fx=0, then Hx=x, which becomes the identity map mentioned above. At this time, the network is equivalent to changing the learning goal. It is no longer learning a complete output, but the difference between the learning goal value Hx and x, i.e., the so-called residual value Fx. Therefore, the subsequent training goal is to approximate the residual value to 0, so that the accuracy will not decrease with the deepening of the network.Although the deep residual network solves the problem that it is difficult to train the deep-seated network and improves the performance of the deep neural network to a certain extent, its accuracy still cannot meet the application in the medical field.


#### 3.1.2. The Second Component of DSRNet50: Block Convolution

Block convolution is to group different characteristic images of the input layer and then use different convolutions to check each group for convolution, which can reduce the amount of convolution calculation, reduce the correlation between channels, and prevent over fitting. Because ordinary convolution is convolution on all input characteristic graphs, i.e., full channel convolution, which is a channel dense connection, packet convolution is a channel sparse connection.Figure2 represents the standard convolution operation. If the dimension of the input characteristic drawing is H×W×c1, the size of convolution kernel is h1×w1×c1, the size of the output characteristic diagram is H×W×c2, and the parameter quantity of standard convolution layer is h1×w1×c1×c2.Figure 2
Ordinary convolution process.Figure3 represents a packet convolution operation. If the input feature map is divided into g groups according to the number of channels, the size of each group of input feature map is H×W×c1/g. The corresponding convolution kernel size is h1×w1×c1/g. The size of each group of output characteristic diagram is H×W×c2/g. Splice the results of group g to obtain the final size of H×W×c2 output characteristic diagram. The parameter quantity of the block convolution layer is h1×w1×c1/g×c2/g×g=h1×w1×c1×c2×1/g.Figure 3
Grouped convolution process.On the characteristic graph of ordinary convolution output, each point is represented by the input characteristic graphh1×w1×c1 points that are calculated, and on the characteristic graph of the block convolution output, each point is calculated by the input characteristic graph of h1×w1×c1/g points. Therefore, the parameter quantity of ordinary convolution is g times of block convolutions.However, considering the lack of information exchange between channels after adding block convolution, which may lead to the loss of information,1×1 convolution kernel and channel attention mechanism are adopted after channel splicing to ensure the information exchange between the characteristic maps of different groups, as is shown in Figure 4.Figure 4
Structural diagram of grouped convolution.


#### 3.1.3. The Third Component of DSRNet50: Channel Attention Mechanism

The channel attention mechanism helps the network locate the channels that have extracted important features and give higher weights. In this paper, the Se module [25] is used to recalibrate the features of each channel, i.e., the redistribution of weights, to help the network learn important feature information, as is shown in Figure 5.Figure 5
Structure diagram of SE module.(1) Squeeze. Feature compression is carried out in the spatial dimension to turn each two-dimensional feature channel into a real number, which has a global receptive field to some extent, and the output dimension matches the number of input feature channels. It represents the characteristic channel responding to the global distribution and enables the layer close to the input to obtain the global receptive field.This operation uses the global average pooling, i.e., the sum of all pixels of a feature map is averaged to obtain a value, which is used to represent a feature map, and the input characteristics ofH×W×C are synthesized as the feature description of 1×1×C. The calculation of a feature map is as follows:
(1)zc=Fsquc=1H×W∑i=1H∑j=1Wuc.(2) Excitation. After squeeze operation, the network only gets a global description, and this result cannot be used as the weight of the channel. The exception operation can comprehensively obtain the dependency relationship between channels. This operation includes two full connection layers and two activation functions; so, it has more nonlinearity to fit the complex correlation between channels. It can integrate all the input characteristic information well and map the input to the 0~1 interval to realize the gating mechanism. The mathematical expression of exception operation is shown in equation (2):
(2)s=Fexz,W=σgz,W=σW2δW1z.In the formula,z is the global description obtained by the squeeze operation, δ is the ReLU function, W1 and W2 are two full connection layers, in which W1∈RC/r×C, W2∈RC/r×C, and r are the scaling parameters, which is mainly used to reduce the computation complexity and parameter quantity of the network, and also affect the experimental accuracy. In this paper, r is selected as 16, taking into account the accuracy and parameter quantity.(3) Reweight. The reweight operation takes the result of the exception output as the importance of each feature channel after feature selection, i.e., the weight coefficient, and then multiplies it to each feature channel one by one to complete the recalibration of the original feature in the channel dimension. The calculation formula of this operation is as follows:
(3)X~=Fscaleuc,SC=SC∗uc.The characteristic graphuc of each channel is multiplied by the corresponding scalar SC. The element SC in the vector obtained in the exception operation is used as the weight coefficient of the corresponding channel.


#### 3.1.4. The Fourth Component of DSRNet50: Dense Connection Path

Inspired by DenseNet [26], this paper will adopt a new dense connection mode, which is similar to the feature fusion of local two paths, combined with the residual network structure to reuse the features, so as to learn and create new features.(1) DenseNet and Higher Order Recurrent Neural Network. HORNN (higher order recurrent neural network) can be generalized to formula (4). In the formula, hk is the current state, ht represents the t-th state in the structure (t<k), ftk. represents the process of extracting features related to the k-th state from ht, xt represents the input data of the t-th state, where x0=h0, and gk. represents that the current state hk is obtained by aggregating all previous states and then processing them with gk.. If ftk. =ft. and gk.=g. for any k and t, formula (4) is an ordinary HORNN.
(4)hk=gk∑t=0k−1ftkht.The output of the previous layers is spliced in the feature dimension, and then1×1 convolution is performed. This operation can be equivalent to doing 1×1 convolution on the direct line of each layer first. The convolution coefficients of 1×1 are different on each straight line and then were arithmetically added, which fully corresponds to formula (4). Therefore, DenseNet is a special HORNN when ftk.=ft. and gk.=g. are not satisfied. Among them, fk−1k, fk−2k, fk−3k, … are different, which means that the features extracted from the front layer are no longer simply reused by the back layer, but create new features. However, the features extracted by convolution in the back layer of this structure are likely to have been extracted by the front layer.(2) DenseNet and Residual Network. Deform formula (4), introduce an intermediate variable rk, and then assume that for any t and k, ftk.=ft., formula (4) can be changed into the expression of formulas (5) and (6):
(5)rk≜∑t=1k‐1ftht=rk−1+fk−1hk−1,(6)hk=gkrk.Combining the above two expressions together gives formula (7), where Φk−1rk−1=fk−1gk−1.. It can be seen that this is a residual expression:
(7)rk=rk‐1+fk−1hk−1=rk‐1+fk−1gk−1rk−1=rk‐1+ϕk−1rk−1.Because formula (5) is derived from formula (4) on the premise of ftk.=ft., ResNet can be considered as a special expression of DenseNet when ftk.=ft..


### 3.2. The Proposed DSRNet50 Structure

In this paper, the local dual path architecture is adopted, and the residual structure is still used as the main network, which is composed of stacking multiple DSR residual blocks. As shown in Figure6, first pass through a convolution layer of 1×1, connecting a block convolution layer of 3×3, and then splice the output of the block convolution. The spliced output will be divided into two channels, and one through the convolution layer of 1×1 is sent to the SE module for feature recalibration and added to the residual path in the form of elements. Another way through another convolution kernel of 1×1 is connected with the dense connection path.Figure 6
Structural diagram of DSR residual block.The residual network reuses the features extracted from the previous layer. In addition to these directly connected reuse features, the features really extracted by convolution are basically new features that have not been extracted before, and the redundancy of the extracted features is relatively low. The features extracted through dense connection are no longer simply reused by the later layer but create new features. However, this structure will lead to that the features extracted by the later convolution layer are likely to have been extracted by the previous layer, and the extracted features have high redundancy. DSRNet combined the advantages of the two networks and added packet convolution and channel attention mechanism, so that it can have good computational characteristics while maintaining high reuse rate and low redundancy of features and continuously explore new features and suppress useless features.


## 3.1. Principle of DSRNet50

### 3.1.1. The First Component of DSRNet50: ResNet50

For deep neural networks, it is generally believed that the deeper the number of network layers, the stronger the nonlinear expression ability of the network, and the network can learn more features; so, the accuracy should be higher. However, it is found that this is not the case in the experimental process. With the increase of the number of layers, the training effect of the traditional convolutional neural network will become worse, and the accuracy will become lower. In order to solve this problem, in 2015, He and others proposed the deep residual network ResNet50 [24]. ResNet50 is a deep residual network which has 50 network layers.The design idea of ResNet50 is as follows: assuming that a networkA can work well, and now there is a network B which is deeper than A, let the former part of B be exactly the same as A, so that the latter part only needs to realize an identity mapping, so that B can at least obtain the same performance as A. As shown in Figure 1, assuming that the input of A certain neural network is x and the expected output is Hx, i.e., Hx is the expected complex potential mapping, and it will be more difficult to learn such a model.Figure 1
Structural diagram of residual block.Back to the previous hypothesis, when the learning accuracy is saturated, or when the error of the lower layer is found to be larger, the next learning goal can be transformed into the learning of identity mapping, even if the inputx is similar to the output Hx, so that the accuracy of the network will not decline in the later level.In the residual network structure diagram shown in Figure1, the input x is directly transmitted to the output as the initial result through “shortcut connections,” and the output is Hx=Fx+x. if Fx=0, then Hx=x, which becomes the identity map mentioned above. At this time, the network is equivalent to changing the learning goal. It is no longer learning a complete output, but the difference between the learning goal value Hx and x, i.e., the so-called residual value Fx. Therefore, the subsequent training goal is to approximate the residual value to 0, so that the accuracy will not decrease with the deepening of the network.Although the deep residual network solves the problem that it is difficult to train the deep-seated network and improves the performance of the deep neural network to a certain extent, its accuracy still cannot meet the application in the medical field.


### 3.1.2. The Second Component of DSRNet50: Block Convolution

Block convolution is to group different characteristic images of the input layer and then use different convolutions to check each group for convolution, which can reduce the amount of convolution calculation, reduce the correlation between channels, and prevent over fitting. Because ordinary convolution is convolution on all input characteristic graphs, i.e., full channel convolution, which is a channel dense connection, packet convolution is a channel sparse connection.Figure2 represents the standard convolution operation. If the dimension of the input characteristic drawing is H×W×c1, the size of convolution kernel is h1×w1×c1, the size of the output characteristic diagram is H×W×c2, and the parameter quantity of standard convolution layer is h1×w1×c1×c2.Figure 2
Ordinary convolution process.Figure3 represents a packet convolution operation. If the input feature map is divided into g groups according to the number of channels, the size of each group of input feature map is H×W×c1/g. The corresponding convolution kernel size is h1×w1×c1/g. The size of each group of output characteristic diagram is H×W×c2/g. Splice the results of group g to obtain the final size of H×W×c2 output characteristic diagram. The parameter quantity of the block convolution layer is h1×w1×c1/g×c2/g×g=h1×w1×c1×c2×1/g.Figure 3
Grouped convolution process.On the characteristic graph of ordinary convolution output, each point is represented by the input characteristic graphh1×w1×c1 points that are calculated, and on the characteristic graph of the block convolution output, each point is calculated by the input characteristic graph of h1×w1×c1/g points. Therefore, the parameter quantity of ordinary convolution is g times of block convolutions.However, considering the lack of information exchange between channels after adding block convolution, which may lead to the loss of information,1×1 convolution kernel and channel attention mechanism are adopted after channel splicing to ensure the information exchange between the characteristic maps of different groups, as is shown in Figure 4.Figure 4
Structural diagram of grouped convolution.


### 3.1.3. The Third Component of DSRNet50: Channel Attention Mechanism

The channel attention mechanism helps the network locate the channels that have extracted important features and give higher weights. In this paper, the Se module [25] is used to recalibrate the features of each channel, i.e., the redistribution of weights, to help the network learn important feature information, as is shown in Figure 5.Figure 5
Structure diagram of SE module.(1) Squeeze. Feature compression is carried out in the spatial dimension to turn each two-dimensional feature channel into a real number, which has a global receptive field to some extent, and the output dimension matches the number of input feature channels. It represents the characteristic channel responding to the global distribution and enables the layer close to the input to obtain the global receptive field.This operation uses the global average pooling, i.e., the sum of all pixels of a feature map is averaged to obtain a value, which is used to represent a feature map, and the input characteristics ofH×W×C are synthesized as the feature description of 1×1×C. The calculation of a feature map is as follows:
(1)zc=Fsquc=1H×W∑i=1H∑j=1Wuc.(2) Excitation. After squeeze operation, the network only gets a global description, and this result cannot be used as the weight of the channel. The exception operation can comprehensively obtain the dependency relationship between channels. This operation includes two full connection layers and two activation functions; so, it has more nonlinearity to fit the complex correlation between channels. It can integrate all the input characteristic information well and map the input to the 0~1 interval to realize the gating mechanism. The mathematical expression of exception operation is shown in equation (2):
(2)s=Fexz,W=σgz,W=σW2δW1z.In the formula,z is the global description obtained by the squeeze operation, δ is the ReLU function, W1 and W2 are two full connection layers, in which W1∈RC/r×C, W2∈RC/r×C, and r are the scaling parameters, which is mainly used to reduce the computation complexity and parameter quantity of the network, and also affect the experimental accuracy. In this paper, r is selected as 16, taking into account the accuracy and parameter quantity.(3) Reweight. The reweight operation takes the result of the exception output as the importance of each feature channel after feature selection, i.e., the weight coefficient, and then multiplies it to each feature channel one by one to complete the recalibration of the original feature in the channel dimension. The calculation formula of this operation is as follows:
(3)X~=Fscaleuc,SC=SC∗uc.The characteristic graphuc of each channel is multiplied by the corresponding scalar SC. The element SC in the vector obtained in the exception operation is used as the weight coefficient of the corresponding channel.


### 3.1.4. The Fourth Component of DSRNet50: Dense Connection Path

Inspired by DenseNet [26], this paper will adopt a new dense connection mode, which is similar to the feature fusion of local two paths, combined with the residual network structure to reuse the features, so as to learn and create new features.(1) DenseNet and Higher Order Recurrent Neural Network. HORNN (higher order recurrent neural network) can be generalized to formula (4). In the formula, hk is the current state, ht represents the t-th state in the structure (t<k), ftk. represents the process of extracting features related to the k-th state from ht, xt represents the input data of the t-th state, where x0=h0, and gk. represents that the current state hk is obtained by aggregating all previous states and then processing them with gk.. If ftk. =ft. and gk.=g. for any k and t, formula (4) is an ordinary HORNN.
(4)hk=gk∑t=0k−1ftkht.The output of the previous layers is spliced in the feature dimension, and then1×1 convolution is performed. This operation can be equivalent to doing 1×1 convolution on the direct line of each layer first. The convolution coefficients of 1×1 are different on each straight line and then were arithmetically added, which fully corresponds to formula (4). Therefore, DenseNet is a special HORNN when ftk.=ft. and gk.=g. are not satisfied. Among them, fk−1k, fk−2k, fk−3k, … are different, which means that the features extracted from the front layer are no longer simply reused by the back layer, but create new features. However, the features extracted by convolution in the back layer of this structure are likely to have been extracted by the front layer.(2) DenseNet and Residual Network. Deform formula (4), introduce an intermediate variable rk, and then assume that for any t and k, ftk.=ft., formula (4) can be changed into the expression of formulas (5) and (6):
(5)rk≜∑t=1k‐1ftht=rk−1+fk−1hk−1,(6)hk=gkrk.Combining the above two expressions together gives formula (7), where Φk−1rk−1=fk−1gk−1.. It can be seen that this is a residual expression:
(7)rk=rk‐1+fk−1hk−1=rk‐1+fk−1gk−1rk−1=rk‐1+ϕk−1rk−1.Because formula (5) is derived from formula (4) on the premise of ftk.=ft., ResNet can be considered as a special expression of DenseNet when ftk.=ft..


## 3.1.1. The First Component of DSRNet50: ResNet50

For deep neural networks, it is generally believed that the deeper the number of network layers, the stronger the nonlinear expression ability of the network, and the network can learn more features; so, the accuracy should be higher. However, it is found that this is not the case in the experimental process. With the increase of the number of layers, the training effect of the traditional convolutional neural network will become worse, and the accuracy will become lower. In order to solve this problem, in 2015, He and others proposed the deep residual network ResNet50 [24]. ResNet50 is a deep residual network which has 50 network layers.The design idea of ResNet50 is as follows: assuming that a networkA can work well, and now there is a network B which is deeper than A, let the former part of B be exactly the same as A, so that the latter part only needs to realize an identity mapping, so that B can at least obtain the same performance as A. As shown in Figure 1, assuming that the input of A certain neural network is x and the expected output is Hx, i.e., Hx is the expected complex potential mapping, and it will be more difficult to learn such a model.Figure 1
Structural diagram of residual block.Back to the previous hypothesis, when the learning accuracy is saturated, or when the error of the lower layer is found to be larger, the next learning goal can be transformed into the learning of identity mapping, even if the inputx is similar to the output Hx, so that the accuracy of the network will not decline in the later level.In the residual network structure diagram shown in Figure1, the input x is directly transmitted to the output as the initial result through “shortcut connections,” and the output is Hx=Fx+x. if Fx=0, then Hx=x, which becomes the identity map mentioned above. At this time, the network is equivalent to changing the learning goal. It is no longer learning a complete output, but the difference between the learning goal value Hx and x, i.e., the so-called residual value Fx. Therefore, the subsequent training goal is to approximate the residual value to 0, so that the accuracy will not decrease with the deepening of the network.Although the deep residual network solves the problem that it is difficult to train the deep-seated network and improves the performance of the deep neural network to a certain extent, its accuracy still cannot meet the application in the medical field.


## 3.1.2. The Second Component of DSRNet50: Block Convolution

Block convolution is to group different characteristic images of the input layer and then use different convolutions to check each group for convolution, which can reduce the amount of convolution calculation, reduce the correlation between channels, and prevent over fitting. Because ordinary convolution is convolution on all input characteristic graphs, i.e., full channel convolution, which is a channel dense connection, packet convolution is a channel sparse connection.Figure2 represents the standard convolution operation. If the dimension of the input characteristic drawing is H×W×c1, the size of convolution kernel is h1×w1×c1, the size of the output characteristic diagram is H×W×c2, and the parameter quantity of standard convolution layer is h1×w1×c1×c2.Figure 2
Ordinary convolution process.Figure3 represents a packet convolution operation. If the input feature map is divided into g groups according to the number of channels, the size of each group of input feature map is H×W×c1/g. The corresponding convolution kernel size is h1×w1×c1/g. The size of each group of output characteristic diagram is H×W×c2/g. Splice the results of group g to obtain the final size of H×W×c2 output characteristic diagram. The parameter quantity of the block convolution layer is h1×w1×c1/g×c2/g×g=h1×w1×c1×c2×1/g.Figure 3
Grouped convolution process.On the characteristic graph of ordinary convolution output, each point is represented by the input characteristic graphh1×w1×c1 points that are calculated, and on the characteristic graph of the block convolution output, each point is calculated by the input characteristic graph of h1×w1×c1/g points. Therefore, the parameter quantity of ordinary convolution is g times of block convolutions.However, considering the lack of information exchange between channels after adding block convolution, which may lead to the loss of information,1×1 convolution kernel and channel attention mechanism are adopted after channel splicing to ensure the information exchange between the characteristic maps of different groups, as is shown in Figure 4.Figure 4
Structural diagram of grouped convolution.


## 3.1.3. The Third Component of DSRNet50: Channel Attention Mechanism

The channel attention mechanism helps the network locate the channels that have extracted important features and give higher weights. In this paper, the Se module [25] is used to recalibrate the features of each channel, i.e., the redistribution of weights, to help the network learn important feature information, as is shown in Figure 5.Figure 5
Structure diagram of SE module.(1) Squeeze. Feature compression is carried out in the spatial dimension to turn each two-dimensional feature channel into a real number, which has a global receptive field to some extent, and the output dimension matches the number of input feature channels. It represents the characteristic channel responding to the global distribution and enables the layer close to the input to obtain the global receptive field.This operation uses the global average pooling, i.e., the sum of all pixels of a feature map is averaged to obtain a value, which is used to represent a feature map, and the input characteristics ofH×W×C are synthesized as the feature description of 1×1×C. The calculation of a feature map is as follows:
(1)zc=Fsquc=1H×W∑i=1H∑j=1Wuc.(2) Excitation. After squeeze operation, the network only gets a global description, and this result cannot be used as the weight of the channel. The exception operation can comprehensively obtain the dependency relationship between channels. This operation includes two full connection layers and two activation functions; so, it has more nonlinearity to fit the complex correlation between channels. It can integrate all the input characteristic information well and map the input to the 0~1 interval to realize the gating mechanism. The mathematical expression of exception operation is shown in equation (2):
(2)s=Fexz,W=σgz,W=σW2δW1z.In the formula,z is the global description obtained by the squeeze operation, δ is the ReLU function, W1 and W2 are two full connection layers, in which W1∈RC/r×C, W2∈RC/r×C, and r are the scaling parameters, which is mainly used to reduce the computation complexity and parameter quantity of the network, and also affect the experimental accuracy. In this paper, r is selected as 16, taking into account the accuracy and parameter quantity.(3) Reweight. The reweight operation takes the result of the exception output as the importance of each feature channel after feature selection, i.e., the weight coefficient, and then multiplies it to each feature channel one by one to complete the recalibration of the original feature in the channel dimension. The calculation formula of this operation is as follows:
(3)X~=Fscaleuc,SC=SC∗uc.The characteristic graphuc of each channel is multiplied by the corresponding scalar SC. The element SC in the vector obtained in the exception operation is used as the weight coefficient of the corresponding channel.


## 3.1.4. The Fourth Component of DSRNet50: Dense Connection Path

Inspired by DenseNet [26], this paper will adopt a new dense connection mode, which is similar to the feature fusion of local two paths, combined with the residual network structure to reuse the features, so as to learn and create new features.(1) DenseNet and Higher Order Recurrent Neural Network. HORNN (higher order recurrent neural network) can be generalized to formula (4). In the formula, hk is the current state, ht represents the t-th state in the structure (t<k), ftk. represents the process of extracting features related to the k-th state from ht, xt represents the input data of the t-th state, where x0=h0, and gk. represents that the current state hk is obtained by aggregating all previous states and then processing them with gk.. If ftk. =ft. and gk.=g. for any k and t, formula (4) is an ordinary HORNN.
(4)hk=gk∑t=0k−1ftkht.The output of the previous layers is spliced in the feature dimension, and then1×1 convolution is performed. This operation can be equivalent to doing 1×1 convolution on the direct line of each layer first. The convolution coefficients of 1×1 are different on each straight line and then were arithmetically added, which fully corresponds to formula (4). Therefore, DenseNet is a special HORNN when ftk.=ft. and gk.=g. are not satisfied. Among them, fk−1k, fk−2k, fk−3k, … are different, which means that the features extracted from the front layer are no longer simply reused by the back layer, but create new features. However, the features extracted by convolution in the back layer of this structure are likely to have been extracted by the front layer.(2) DenseNet and Residual Network. Deform formula (4), introduce an intermediate variable rk, and then assume that for any t and k, ftk.=ft., formula (4) can be changed into the expression of formulas (5) and (6):
(5)rk≜∑t=1k‐1ftht=rk−1+fk−1hk−1,(6)hk=gkrk.Combining the above two expressions together gives formula (7), where Φk−1rk−1=fk−1gk−1.. It can be seen that this is a residual expression:
(7)rk=rk‐1+fk−1hk−1=rk‐1+fk−1gk−1rk−1=rk‐1+ϕk−1rk−1.Because formula (5) is derived from formula (4) on the premise of ftk.=ft., ResNet can be considered as a special expression of DenseNet when ftk.=ft..


## 3.2. The Proposed DSRNet50 Structure

In this paper, the local dual path architecture is adopted, and the residual structure is still used as the main network, which is composed of stacking multiple DSR residual blocks. As shown in Figure6, first pass through a convolution layer of 1×1, connecting a block convolution layer of 3×3, and then splice the output of the block convolution. The spliced output will be divided into two channels, and one through the convolution layer of 1×1 is sent to the SE module for feature recalibration and added to the residual path in the form of elements. Another way through another convolution kernel of 1×1 is connected with the dense connection path.Figure 6
Structural diagram of DSR residual block.The residual network reuses the features extracted from the previous layer. In addition to these directly connected reuse features, the features really extracted by convolution are basically new features that have not been extracted before, and the redundancy of the extracted features is relatively low. The features extracted through dense connection are no longer simply reused by the later layer but create new features. However, this structure will lead to that the features extracted by the later convolution layer are likely to have been extracted by the previous layer, and the extracted features have high redundancy. DSRNet combined the advantages of the two networks and added packet convolution and channel attention mechanism, so that it can have good computational characteristics while maintaining high reuse rate and low redundancy of features and continuously explore new features and suppress useless features.


## 4. Experiment and Analysis

This section mainly describes the datasets cited in this paper, as well as the preprocessing methods for datasets to prevent over fitting. Finally, taking the general evaluation method as the index, the advantages and disadvantages of different algorithms in the index are tested.


### 4.1. Dataset

Herlev cervical cell dataset [11] was used by Herlev University Hospital (HUH) and Technical University of Denmark (TUD) with a resolution of 0.201 μM digital camera and microscope to collect the new version of Herlev Pap smear dataset. There are 917 single-cell images in the dataset, including 242 normal cells and 675 abnormal cells. The specific number is shown in Table 1.Table 1
Herlev cervical cell dataset.
TypeNameNumber of imagesNormalNormal superficile74Normal intermediate70Normal columnar98AbnormalLightweight dyspastic182Moderate dayspatial146Server dayspatial197Carcinoma150It can be seen from Figure7 that the nuclear cytoplasmic ratio and nuclear cytoplasmic brightness are obvious characteristic differences. The images of seven types of cervical cells are different in shape, among which Figures 7(a)–7(c) are normal cells and Figures 7(d)–7(g) are diseased cells. Most normal cells are quite different from abnormal cells, columnar epithelial cells, squamous cell carcinoma in situ, and severely squamous intraepithelial diseased cells, and the similarity between middle squamous cells and epithelial squamous cells is high. It is difficult to distinguish.Figure 7
7 types of cervical cell images: (a) normal superficile, (b) normal intermediate, (c) normal columnar, (d) lightweight dyspastic, (e) moderate dayspatial, (f) server dayspatial, and (g) carcinoma.
(a)(b)(c)(d)(e)(f)(g)


### 4.2. Dataset Preprocessing

Data expansion is a common method in deep learning, that is, making a certain transformation on the image data to increase the number of input data, which can prevent the overfitting phenomenon due to less data. However, due to the particularity of cervical cells, the common color transformation, scale transformation, and noise transformation in data expansion will affect the original characteristics of cervical cells; so, it is not applied to the expansion of this type of dataset. All images are processed in strict accordance with the standard of TBS2014. In this paper, the methods of flip and rotation are used to expand, and then all the images in the dataset are processed into224×224 pixel images. The number of cell images in the expanded dataset is shown in Table 2.Table 2
Extended Herlev cervical cell dataset.
TypeNameNumber of imagesNormalNormal superficile370Normal intermediate350Normal columnar490AbnormalLightweight dyspastic910Moderate dayspatial730Server dayspatial985Carcinoma750


### 4.3. Evaluating Indicator

This paper takes the expanded dataset as the training set and the original dataset as the test set. The evaluation method is generally to compare the real situation of patients with the diagnosis of the system. The diagnosis results include four categories, namely,True negative (TN): the true condition is no lesion, and the diagnosis is no lesionFalse negative (FN): the real situation is lesion, and the diagnosis is no lesionFalse positive (FP): the real situation is no lesion, and the diagnosis is lesionTrue positive (TP): the real situation is lesion, and the diagnosis is lesionThe evaluation indicators also include four categories: accuracy (ACC), that is, the proportion of correctly classified samples in the total samples; the accuracy rate (P) is how many samples predicted as positive examples are real positive examples; recall rate (R), that is, how many positive examples in the sample are predicted correctly, i.e., sensitivity; and F1-score is the weighted harmonic average value of P and R, and it is an index proposed considering the unbalanced number of samples in each classification. When F1-score is high, the expression method is more effective. Since this paper realizes seven classifications of cervical cell images, the total index of the model needs to be obtained after the sum of the indexes of each classification. The calculation formula of four indicators is shown in Table 3:Table 3
Standard formula of evaluation index.
Evaluation indexFormulaACCTP+TN/TP+TN+FP+FNPTP/TP+FPRTP/TP+FNF12×P×R/P+R


### 4.4. Experimental Results and Analysis

All experimental results in this section are obtained in the same environment. Experimental environment configuration is as follows: the operating system is windows 8.1 ×64. The CPU is Intel Xeon 8-core 2620v4×2; GPU is NVIDIA GTX 1080ti and 16 GB memory. The framework is TensorFlow 3.0. The programming language is Python 3.6.Test seven classification performances of Herlev dataset on the proposed DSRNet50. Set epoch to 100, which means 100 rounds of iteration. The scaling parameter isR=16, and the grouping convolution parameter is G=32. As shown in Figure 8, the proposed DSRNet50 gradually converges with the increase of the number of iterations. After about 20 full set iterations, the curve tends to be flat.Figure 8
Accuracy/loss value curve.In order to fully illustrate the effectiveness of this method, the structure of different methods will be adjusted based on the original RESNet50 network, and a comparative experiment will be carried out on the Herlev dataset. It can be seen from Table4 that the results of several improved networks using different methods are compared. GC represents packet convolution, Se represents channel attention module, and D represents dense connection. N1 is the original RESNet50 network, N2 to N4 are improved networks using different methods, and N5 is the proposed DSRNet50 network in this paper. It can be seen from the table that after adding different methods, the performance of the network has been improved to varying degrees. Compared with N1, N2 added with channel attention module has improved the ACC, P, and R by 2.9%, 2.1%, and 2.5%, respectively, and then N3 has further improved by 1.2%, 1.3%, and 0.6% after adding grouping convolution on the basis of N2. After adding dense connection to N2, N4 increased by 0.6%, 0.5%, and 0.7% compared with N3. Compared with N1, the ACC, P, and R of algorithm N5 in this paper are improved by 6.6%, 5.5%, and 5.2%, respectively, which shows the effectiveness of this method in the detection of cervical lesion cells.Table 4
Model evaluation indicators of different methods.
MethodEvaluation indicatorsModel sizeResNet50GCSEDACC/%P/%R/%F1/%ParametersFLOPsN1√92.393.793.693.523.5×1063.86×109N2√√95.295.896.195.824.1×1063.87×109N3√√√96.497.196.796.923.7×1063.97×109N4√√√97.097.697.497.525.1×1064.12×109N5√√√√98.999.298.899.024.7×1064.14×109In terms of parameters and calculation amount, since the number of channels in the last layer is 2048, the model has a large increase in parameters; so, the SE module is removed in the last layer, which has little impact on the ACC. As shown in Table4, N5 has an increase of about 5% in parameters compared with N1, and the amount of calculation has increased by about 7%. It can be seen that the proposed DSRNet50 improves the accuracy and only adds small parameters and amount of calculation, which is acceptable.Table5 shows the detailed indicators of seven classifications of the proposed DSRNet50 in the test set. It can be seen from the table that the proposed DSRNet50 shows good classification performance and can well distinguish various types of pathological cells, but the classification effect of severe squamous intraepithelial lesions and squamous cell carcinoma in situ is slightly poor, indicating that the characteristics of these two types of cells are highly similar to those of other cells, so that the network cannot distinguish their pathological types sometimes.Table 5
The proposed DSRNet50 test set classification evaluation indicators.
Cell typeP/%R/%F1/%Normal superficile100.097.799.3Normal intermediate99.3100.099.3Normal columnar100.099.099.5Lightweight dyspastic99.598.898.9Server dayspatial99.498.699.2Server dayspatial97.099.597.8Carcinoma97.298.797.7In order to further illustrate the performance of this algorithm in the field of cervical cytopathy detection, this algorithm is compared with the performance of other references on the Herlev dataset. Because most algorithms on the Herlev dataset are the two classifications of cervical cells with or without lesions, they cannot be directly compared in performance, but for cells, multiclassification is more persuasive than two classifications. It can better illustrate the accuracy of the algorithm. The comparison results are shown in Table6. The algorithm in this paper shows comparable or even better performance with other two classification and seven classification algorithms, which is enough to illustrate the effectiveness of this method.Table 6
Performance comparison between the proposed DSRNet50 and other algorithms.
MethodNumber of categoriesACC/%P/%R/%Marinakis et al. [13]296.892.198.5Jantzen et al. [11]293.679.398.8Bora et al. [27]296.589.799.3Kim et al. [28]293.681.398.3Zhang et al. [20]798.398.398.2Ours798.999.298.8To sum up, for this paper, the difficulty is to achieve excellent classification effect in multiclassification of the same thing. In medical assisted diagnosis, accuracy and recall are the most critical evaluation indicators. According to the experimental results in this section, this method has better classification performance than other two classification algorithms in the seven classification tasks of cervical cells. Literature [13] uses genetic algorithm and 1-neighbor algorithm for classification. Literature [11] extracts feature classification after presegmenting images. Literature [27] uses artificial neural network ANN for classification. Literature [11] designed and used three classifiers to vote for classification. Literature [20] uses convolutional neural network to automatically extract features for classification. Although literature [11, 13, 27] has high accuracy, it needs to design features manually. The accuracy of literature [11, 27, 28] is low, which shows that the ability to correctly predict positive cases is poor. The average error rate of this algorithm on Herlev dataset is 1.5%. Compared with other binary classification algorithms, this algorithm has more practical value in the field of auxiliary diagnosis. In terms of seven categories, the proposed algorithm improves ACC, P, and R by 0.5%, 0.9%, and 0.5%, respectively. For tens of thousands of cases in the future, a considerable number of families can be saved from later treatment [20]. In general, the effectiveness of the proposed DSRNet50 is proved by the experiment.


## 4.1. Dataset

Herlev cervical cell dataset [11] was used by Herlev University Hospital (HUH) and Technical University of Denmark (TUD) with a resolution of 0.201 μM digital camera and microscope to collect the new version of Herlev Pap smear dataset. There are 917 single-cell images in the dataset, including 242 normal cells and 675 abnormal cells. The specific number is shown in Table 1.Table 1
Herlev cervical cell dataset.
TypeNameNumber of imagesNormalNormal superficile74Normal intermediate70Normal columnar98AbnormalLightweight dyspastic182Moderate dayspatial146Server dayspatial197Carcinoma150It can be seen from Figure7 that the nuclear cytoplasmic ratio and nuclear cytoplasmic brightness are obvious characteristic differences. The images of seven types of cervical cells are different in shape, among which Figures 7(a)–7(c) are normal cells and Figures 7(d)–7(g) are diseased cells. Most normal cells are quite different from abnormal cells, columnar epithelial cells, squamous cell carcinoma in situ, and severely squamous intraepithelial diseased cells, and the similarity between middle squamous cells and epithelial squamous cells is high. It is difficult to distinguish.Figure 7
7 types of cervical cell images: (a) normal superficile, (b) normal intermediate, (c) normal columnar, (d) lightweight dyspastic, (e) moderate dayspatial, (f) server dayspatial, and (g) carcinoma.
(a)(b)(c)(d)(e)(f)(g)


## 4.2. Dataset Preprocessing

Data expansion is a common method in deep learning, that is, making a certain transformation on the image data to increase the number of input data, which can prevent the overfitting phenomenon due to less data. However, due to the particularity of cervical cells, the common color transformation, scale transformation, and noise transformation in data expansion will affect the original characteristics of cervical cells; so, it is not applied to the expansion of this type of dataset. All images are processed in strict accordance with the standard of TBS2014. In this paper, the methods of flip and rotation are used to expand, and then all the images in the dataset are processed into224×224 pixel images. The number of cell images in the expanded dataset is shown in Table 2.Table 2
Extended Herlev cervical cell dataset.
TypeNameNumber of imagesNormalNormal superficile370Normal intermediate350Normal columnar490AbnormalLightweight dyspastic910Moderate dayspatial730Server dayspatial985Carcinoma750


## 4.3. Evaluating Indicator

This paper takes the expanded dataset as the training set and the original dataset as the test set. The evaluation method is generally to compare the real situation of patients with the diagnosis of the system. The diagnosis results include four categories, namely,True negative (TN): the true condition is no lesion, and the diagnosis is no lesionFalse negative (FN): the real situation is lesion, and the diagnosis is no lesionFalse positive (FP): the real situation is no lesion, and the diagnosis is lesionTrue positive (TP): the real situation is lesion, and the diagnosis is lesionThe evaluation indicators also include four categories: accuracy (ACC), that is, the proportion of correctly classified samples in the total samples; the accuracy rate (P) is how many samples predicted as positive examples are real positive examples; recall rate (R), that is, how many positive examples in the sample are predicted correctly, i.e., sensitivity; and F1-score is the weighted harmonic average value of P and R, and it is an index proposed considering the unbalanced number of samples in each classification. When F1-score is high, the expression method is more effective. Since this paper realizes seven classifications of cervical cell images, the total index of the model needs to be obtained after the sum of the indexes of each classification. The calculation formula of four indicators is shown in Table 3:Table 3
Standard formula of evaluation index.
Evaluation indexFormulaACCTP+TN/TP+TN+FP+FNPTP/TP+FPRTP/TP+FNF12×P×R/P+R


## 4.4. Experimental Results and Analysis

All experimental results in this section are obtained in the same environment. Experimental environment configuration is as follows: the operating system is windows 8.1 ×64. The CPU is Intel Xeon 8-core 2620v4×2; GPU is NVIDIA GTX 1080ti and 16 GB memory. The framework is TensorFlow 3.0. The programming language is Python 3.6.Test seven classification performances of Herlev dataset on the proposed DSRNet50. Set epoch to 100, which means 100 rounds of iteration. The scaling parameter isR=16, and the grouping convolution parameter is G=32. As shown in Figure 8, the proposed DSRNet50 gradually converges with the increase of the number of iterations. After about 20 full set iterations, the curve tends to be flat.Figure 8
Accuracy/loss value curve.In order to fully illustrate the effectiveness of this method, the structure of different methods will be adjusted based on the original RESNet50 network, and a comparative experiment will be carried out on the Herlev dataset. It can be seen from Table4 that the results of several improved networks using different methods are compared. GC represents packet convolution, Se represents channel attention module, and D represents dense connection. N1 is the original RESNet50 network, N2 to N4 are improved networks using different methods, and N5 is the proposed DSRNet50 network in this paper. It can be seen from the table that after adding different methods, the performance of the network has been improved to varying degrees. Compared with N1, N2 added with channel attention module has improved the ACC, P, and R by 2.9%, 2.1%, and 2.5%, respectively, and then N3 has further improved by 1.2%, 1.3%, and 0.6% after adding grouping convolution on the basis of N2. After adding dense connection to N2, N4 increased by 0.6%, 0.5%, and 0.7% compared with N3. Compared with N1, the ACC, P, and R of algorithm N5 in this paper are improved by 6.6%, 5.5%, and 5.2%, respectively, which shows the effectiveness of this method in the detection of cervical lesion cells.Table 4
Model evaluation indicators of different methods.
MethodEvaluation indicatorsModel sizeResNet50GCSEDACC/%P/%R/%F1/%ParametersFLOPsN1√92.393.793.693.523.5×1063.86×109N2√√95.295.896.195.824.1×1063.87×109N3√√√96.497.196.796.923.7×1063.97×109N4√√√97.097.697.497.525.1×1064.12×109N5√√√√98.999.298.899.024.7×1064.14×109In terms of parameters and calculation amount, since the number of channels in the last layer is 2048, the model has a large increase in parameters; so, the SE module is removed in the last layer, which has little impact on the ACC. As shown in Table4, N5 has an increase of about 5% in parameters compared with N1, and the amount of calculation has increased by about 7%. It can be seen that the proposed DSRNet50 improves the accuracy and only adds small parameters and amount of calculation, which is acceptable.Table5 shows the detailed indicators of seven classifications of the proposed DSRNet50 in the test set. It can be seen from the table that the proposed DSRNet50 shows good classification performance and can well distinguish various types of pathological cells, but the classification effect of severe squamous intraepithelial lesions and squamous cell carcinoma in situ is slightly poor, indicating that the characteristics of these two types of cells are highly similar to those of other cells, so that the network cannot distinguish their pathological types sometimes.Table 5
The proposed DSRNet50 test set classification evaluation indicators.
Cell typeP/%R/%F1/%Normal superficile100.097.799.3Normal intermediate99.3100.099.3Normal columnar100.099.099.5Lightweight dyspastic99.598.898.9Server dayspatial99.498.699.2Server dayspatial97.099.597.8Carcinoma97.298.797.7In order to further illustrate the performance of this algorithm in the field of cervical cytopathy detection, this algorithm is compared with the performance of other references on the Herlev dataset. Because most algorithms on the Herlev dataset are the two classifications of cervical cells with or without lesions, they cannot be directly compared in performance, but for cells, multiclassification is more persuasive than two classifications. It can better illustrate the accuracy of the algorithm. The comparison results are shown in Table6. The algorithm in this paper shows comparable or even better performance with other two classification and seven classification algorithms, which is enough to illustrate the effectiveness of this method.Table 6
Performance comparison between the proposed DSRNet50 and other algorithms.
MethodNumber of categoriesACC/%P/%R/%Marinakis et al. [13]296.892.198.5Jantzen et al. [11]293.679.398.8Bora et al. [27]296.589.799.3Kim et al. [28]293.681.398.3Zhang et al. [20]798.398.398.2Ours798.999.298.8To sum up, for this paper, the difficulty is to achieve excellent classification effect in multiclassification of the same thing. In medical assisted diagnosis, accuracy and recall are the most critical evaluation indicators. According to the experimental results in this section, this method has better classification performance than other two classification algorithms in the seven classification tasks of cervical cells. Literature [13] uses genetic algorithm and 1-neighbor algorithm for classification. Literature [11] extracts feature classification after presegmenting images. Literature [27] uses artificial neural network ANN for classification. Literature [11] designed and used three classifiers to vote for classification. Literature [20] uses convolutional neural network to automatically extract features for classification. Although literature [11, 13, 27] has high accuracy, it needs to design features manually. The accuracy of literature [11, 27, 28] is low, which shows that the ability to correctly predict positive cases is poor. The average error rate of this algorithm on Herlev dataset is 1.5%. Compared with other binary classification algorithms, this algorithm has more practical value in the field of auxiliary diagnosis. In terms of seven categories, the proposed algorithm improves ACC, P, and R by 0.5%, 0.9%, and 0.5%, respectively. For tens of thousands of cases in the future, a considerable number of families can be saved from later treatment [20]. In general, the effectiveness of the proposed DSRNet50 is proved by the experiment.


## 5. Conclusion

Aiming at the problems of low efficiency and low accuracy in the existing detection methods of cervical cytopathy, this paper proposes an improved deep convolution neural network DSRNet, which adopts a dual path structure, takes the residual path as the main body, and adds local dense connection paths, so that the network can constantly explore new features while maintaining the high reuse rate and low redundancy of features. In addition, packet convolution is added to the main network to reduce the amount of calculation and better prevent the phenomenon of overfitting. Then, the SE module is used for feature recalibration to make better use of important features and suppress useless features. Experimental results show that this algorithm can detect seven kinds of normal and diseased cells with high accuracy on Herlev dataset. Compared with other algorithms, it has good performance improvement and fills the defects of ubiquitous computing in WITMED and intelligent diagnosis. In this follow-up work, we can collect more data, improve the reliability of experimental results, consider hardware acceleration research according to the requirements of practical application, and design a practical detection algorithm that takes into account both accuracy and computational complexity.



---

*Source: 8496751-2022-09-12.xml*