# A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning

Authors: Ruqian Hao; Lin Liu; Jing Zhang; Xiangzhou Wang; Juanxiu Liu; Xiaohui Du; Wen He; Jicheng Liao; Lu Liu; Yuanying Mao
Journal: Journal of Healthcare Engineering
 (2022)

---

## Abstract

Vaginitis is a gynecological disease affecting the health of millions of women all over the world. The traditional diagnosis of vaginitis is based on manual microscopy, which is time-consuming and tedious. The deep learning method offers a fast and reliable solution for an automatic early diagnosis of vaginitis. However, deep neural networks require massive well-annotated data. Manual annotation of microscopic images is highly cost extensive because it not only is a time-consuming process but also needs highly trained people (doctors, pathologists, or technicians). Most existing active learning approaches are not applicable in microscopic images due to the nature of complex backgrounds and numerous formed elements. To address the problem of high cost of labeling microscopic images, we present a data-efficient framework for the identification of vaginitis based on transfer learning and active learning strategies. The proposed informative sample selection strategy selected the minimal training subset, and then the pretrained convolutional neural network (CNN) was fine-tuned on the selected subset. The experiment results show that the proposed pipeline can save 37.5% annotation cost while maintaining competitive performance. The proposed promising novel framework can significantly save the annotation cost and has the potential of extending widely to other microscopic imaging applications, such as blood microscopic image analysis.


---

## Body

## 1. Introduction

Vaginitis is an infection or inflammation of the vagina, which threatens the health of millions of women worldwide [1]. There are three common types of infectious vaginitis, which are bacterial vaginosis, candidal vaginitis, and trichomonas vaginitis [2]. These disorders can potentially lead to an increased risk of pelvic inflammatory disease (PID) [3], premature labour [4], human immunodeficiency virus (HIV) infection [5], and so on. Wet mount microscopy is a simple and effective method for diagnosing vaginitis [6]. The clinician collects vaginal discharge from the patient using a cotton-tipped applicator and spreads it on a glass slide with 0.9% NaCl solution. The diagnosis of vaginitis is confirmed by manually observing the quantity and morphology of formed elements in vaginal fluid. However, this diagnostic technique is extremely time-consuming, and it requires doctors to have a high level of professional knowledge, and the diagnosis results are easily affected by the clinician’s subjectivity and experiences. Therefore, a rapid and reliable framework for the automatic diagnosis of vaginitis at an early stage is urgently needed.Various computer-assisted methods have been extensively studied for the identification of vaginitis. The broad applicability of traditional digital image processing algorithms is however limited, because massive parameters in these methods need to be set and optimized manually [7]. For example, Hao et al. [8] presented an automatic detection algorithm for trichomonas vaginalis based on an improved Kalman background reconstruction algorithm, and the sensitivity and specificity reached 95% and 97%, respectively; however, this algorithm heavily relies on manual parameter tuning, such as the area, length, width, eccentricity, and circularity.With the recent advancement in machine learning and deep learning technology, the hybrid approaches combining traditional image processing and artificial intelligence (AI) techniques have become prevalent in the field of medical diagnostics. Song et al. [9] proposed an automatic bacterial vaginosis diagnosis system, which segments the bacteria regions using traditional computer vision algorithm (i.e., saliency cut) and then trains a machine learning Adaptive Boosting model by inputting extracted morphotype features for vaginosis classification. An accuracy of 90.7% was achieved. Nevertheless, the performance of their proposed diagnosis system highly depends on the traditional segmentation results, which need manual parameter tuning. Besides, the average running time for each microscopic image was around 30 s, which did not achieve real-time detection. Zhang et al. [10] trained a convolutional neural network (CNN) to extract features from the microscopic leucorrhea image and then built a support vector machine (SVM) model to classify candidal vaginitis using the histogram of oriented gradients (HOG) features that were exacted from the previous feature maps. However, their presented method achieved very high sensitivity and specificity, which were 99.8% and 95.1%, respectively. However, it needs to manually set the threshold of the segmentation algorithm, and the correctness of the segmentation algorithm is directly related to the subsequent recognition. Wang et al. [11] presented a CNN model to classify three categories of Nugent scores for automating diagnosis of bacterial vaginosis in Gram-stained microscopic vaginal discharge images, and the sensitivity and specificity of the model were 82.4% and 96.6%, respectively. The model inference speed was 25 ms per image, which is faster than that of the conventional image processing method. However, the drawback of this deep learning method is that it requires massive data for training the model, and they used 23,280 samples as training data. Even though this end-to-end framework is that it requires no artificial parameter tuning or previous image segmentation, this approach has an extremely high labeling cost and is subject to the size of annotated data.For medical image diagnostic assistance, deep learning methods can outperform many traditional image processing and machine learning methods due to their efficiency in feature extraction from original data [12, 13]. The performance of deep learning techniques heavily relies on the size and quality of training data, and typically a tremendous amount of labeled data is needed for a high-performance model. Nonetheless, it is challenging to label large quantities of images due to the high cost of time and expertise from experienced clinicians [14]. Transfer learning and active learning strategies have been investigated to address and resolve the above-mentioned challenges. Transfer learning is a technique that pretrains a CNN on a large labeled dataset and then fine-tunes the pretrained CNN on the target dataset, which can effectively speed up network convergence without compromising model performance [15]. Transfer learning has been successfully applied in the field of medical imaging, such as brain tumor classification task [16–19], prostate cancer recognition task [20, 21], and diabetic retinopathy grading task [22]. The active learning algorithm is another effective strategy for minimizing the label cost. By selecting and annotating the most informative samples from the whole unlabeled dataset, the model trained on the selected subset can achieve competitive results compared to that trained on the entire labeled dataset [23]. Zhou et al. [24] measured the uncertainty score of each sample by computing entropy and relative entropy of predicted probabilities, which were obtained by inputting the original image and corresponding morphological transformed image into the pretrained AlexNet [25]. Next, they selected and annotated the most uncertain samples at each iteration during the training process, and at least half of the annotation cost was saved in three different biomedical imaging applications. This approach is not applicable in our scenario because their datasets are radiology images with relatively small size and simple background, while our dataset contains microscopic images with high resolution, complex background, and numerous cells. The morphological transformation of the microscopic image may lose massive detailed information, and the AlexNet structure is too shallow to exact features. Dai et al. [26] presented a gradient-guided suggestive annotation framework using a variational autoencoder (VAE) for the brain tumor segmentation task. They obtained similar results using only 19% of the magnetic resonance imaging (MRI) images compared to the results by using the whole labeled dataset. However, there are several reasons that their proposed framework was not applicable in microscopy image classification. Their informative sample selection method was designed for MRI image segmentation, and the slice size in their dataset was only 240 × 240 pixels, which is relatively smaller than our image size of 1,920 × 1,200 pixels. Training a VAE can cost a high computational load and lead to extremely slow convergence or nonconvergence due to the large size and complex background of the microscopic image. Thus, a practical and efficient annotation reduction method for microscopic images classification is in urgent demand.In this work, we propose a data-efficient framework for the identification of vaginitis based on deep learning. Deep learning techniques can surpass traditional image processing and machine learning methods on inference speed, robustness, and accuracy; however, it requires a huge amount of high-quality labeled data. The proposed framework was designed for microscopic images with large size and complex background based on transfer learning and active learning techniques, which can significantly reduce the quantity of required labeled data while maintaining the model performance. The proposed framework has both theoretical and practical implications. Reducing the annotation cost can not only greatly reduce the burden on doctors but also efficiently shorten the development cycle of medical diagnostic equipment; thus, the computer-aided diagnosis system can be quickly applied in practical use.The rest of the paper is organized as follows: Section2 describes the dataset, the details of the proposed methods, and the evaluation metrics. Section 3 presents the experimental results. The discussion of experiment results, limitations, and future research is described in Section 4. Section 5 describes the conclusion of this study.


## 2. Materials and Methods

### 2.1. Dataset

The microscopic leucorrhea image dataset in this work was collected from the Sixth People’s Hospital of Chengdu, Sichuan Province. There were 229 female patients. All patients in this study had signed informed consent, and the study had been approved by the Medical Ethics Committee of the hospital.The microscopic leucorrhea images were obtained by a CX31 biological microscope (Olympus, Tokyo, Japan) and an EXCCD01400KMA CCD camera (Motic, Xiamen, China). The objective lens was 40x. The pixel size and the exposure time were set to 6.45μm × 6.45 µm and 40 ms, respectively. The field of view (FOV) in this optical system was 0.41 mm × 0.26 mm.There are a total of 1302 microscopic images with a resolution of 1,920 × 1,200 pixels. An experienced pathologist and a gynecologist manually annotated each image. Every normal image was labeled as negative 0, and images with vaginitis were labeled as positive 1. In the dataset, there are 569 positive samples and 733 negative samples. The normal leucorrhea image and the leucorrhea images with three common types of infectious vaginitis are shown in Figure1. We randomly selected 20% of the images as the test dataset (260 images) and the rest as the training dataset (1042 images).Figure 1
Different types of microscopic leucorrhea images. (a) Normal, negative sample. (b) Bacterial vaginosis, positive sample. (c) Candidal vaginitis, positive sample. (d) Trichomonas vaginitis, positive sample.
(a)(b)(c)(d)


### 2.2. Transfer Learning and CNN Architectures

Transfer learning is a data-effective technique for CNN training. It can save both computational resources and training period by transferring the learned knowledge from the massive annotated source dataset to the target dataset. In this work, we employed fine-tuning strategy that adopts the weights and biases from a CNN pretrained on ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) dataset [27] and then carried on a conventional training process on the target dataset. Different network structures vary in layer depth, feature extraction, and representation capabilities, which may result in different outcomes and trends for vaginitis classification. In this research, we opted for four popular and powerful state-of-the-art CNN architectures coupled with transfer learning. Based on the purpose of vaginitis recognition, the last layer of each CNN was replaced with an output layer with two neurons. We employed fine-tuning strategy in all layers instead of several layers because our target microscopic image dataset has very different characteristics from the ImageNet dataset. For the image preprocessing stage, we employed the bilinear interpolation transformation method to resize each microscopic image to 224 × 224 pixels before classification by the CNNs. The purpose of resizing the images is to fit the pretrained model input while saving computation resources and training time. Next, the details of the architectures of AlexNet, VGG16, ResNet50, and se_ResNet50 are described.AlexNet [25] won the ILSVRC competition in 2012 and has revolutionized the field of computer vision. As shown in Figure 2, AlexNet is a relatively shallow network, which contains five convolutional layers, three max-pooling layers, and three fully connected layers. The input image size was 224 × 224. The activation function followed by the convolution layer was Rectified Linear Unit (ReLU), and the Softmax activation function was used before output.Figure 2
AlexNet architecture.VGG [28] was proposed in 2014, and it is the first time deep learning achieved top-5 error under 10% in ILSVRC competition. VGG has a very deep network design, and the kernel size is very small (3 × 3), which decreases the number of parameters while maintaining performance. For example, the receptive field size of two layers of 3 × 3 kernel is equal to that of a single layer of 5 × 5 kernel; however, the number of parameters is cut by 28%. As presented in Figure 3, VGG16 consists of 16 layers that have weights (i.e., 13 convolutional layers and 3 fully connected layers) and 5 max-pooling layers.Figure 3
VGG16 architecture.He et al. [29] empirically showed that deeper neural network does not mean stronger performance due to overfitting and vanishing gradient problem. Therefore, they proposed the residual block to alleviate the problem. Figure 4 indicates the structure of the residual block. The identity mapping is adding the input x to the output F(x) of the multiple convolutional layers block as the final output. The skip connection considers x + F(x) to be the input to the next layer. As illustrated in Figure 5, ResNet50 stacks the residual blocks to construct the deep CNN network, and the skip connections between layers are indicated in blue arrow lines. The residual block and skip connections enable the ResNet to become extremely deep and have empirically shown better performance than previous deep neural networks in ImageNet classification.Figure 4
Residual block.Figure 5
ResNet50 architecture.Hu et al. [30] proposed a squeeze and excitation (SE) block, which improved the CNN performance and won the 2017 ILSVRC competition. The detailed structure of the SE block is shown in Figure 6. The SE block contains two steps: squeeze and excitation. The squeeze step transforms every channel of the input feature map into a single numeric value by global pooling. The excitation step uses two fully connected layers to add the necessary nonlinearity and then uses the output as the weight matrix of each channel to scale the original feature map input. The SE block significantly boosts network performance by helping the network adjust the weights of each feature map. This block can be conveniently added to any existing structure without causing an extra computational burden. As indicated in Figure 7, se_ResNet50 ensembles the SE block into the residual block on the basis of the structure of ResNet50.Figure 6
Squeeze and excitation block.Figure 7
se_ResNet50 architecture.


### 2.3. Active Learning for Informative Sample Selection

Some training examples make the CNN exhibits superior performance compared with other training data. Active learning is a commonly used technique, which explores the minimal subset of the training cohort that enables the neural network to maintain high performance. In this work, we used entropy and Kullback–Leibler (KL) divergence as metrics to compute the uncertainty score of every unlabeled image and then select the most informative samples based on the obtained uncertainty scores.As shown in Figure8, for every unlabeled image X in the training dataset, four fine-tuned CNNs (i.e., AlexNet, VGG16, ResNet50, and se_ResNet50) were employed to obtain the predicted probabilities. Note that the fine-tuned models only perform the inference process instead of the training process. Assuming that Pi and Pj are obtained probabilities from different fine-tuned CNNs, the entropy formula of the image X is given by (1)HX=−∑iPilogPi.Figure 8
Workflow of uncertainty score calculation.The KL divergence formula is given by(2)DKLPiPj=∑i∑jPilogPiPj.The uncertainty score of an unlabeled image is the sum of entropy and KL divergence, and we calculated the uncertainty score for every image in unlabeled training data to obtain the uncertainty score list. The uncertainty score formula is given by(3)USX=αHX+βDKLX.According to the best experimental results, the coefficientsα and β were set to 1. Since there are 4 different probabilities that were obtained in the proposed framework, every unlabeled image has 4 entropy scores and 12 KL divergence scores. The higher the uncertainty score, the more informative the sample. We sorted the uncertainty score list and selected the most informative samples.


### 2.4. Workflow of the Proposed Framework

In this work, we propose a novel data-efficient framework for the identification of vaginitis based on deep learning. The high-quality annotation for microscopic data is extremely time-consuming and requires a huge amount of budget. To address this problem, the proposed framework integrates transfer learning and active learning techniques to achieve competitive CNN performance at the minimum annotation cost. As depicted in Figure9, the workflow is divided into 5 steps:(1)
We randomly selected and labeled 25% of samples from the completed training cohort. The labeled training subset was used to fine-tune the pretrained AlexNet, VGG16, ResNet50, and se_ResNet.(2)
The unlabeled 75% (i.e., 1–25%) training data was input to the four fine-tuned CNNs to obtain the predicted probabilities. Note that the fine-tuned models only perform the inference process to calculate outputs without backpropagation.(3)
We used the obtained probability list to calculate the uncertainty score (i.e., entropy and KL divergence), which measures the informativeness of each unlabeled training sample. Then, we selected k% of the unlabeled training data as the most informative samples according to the sorted uncertainty score list.(4)
We annotated the selected informative samples and added them to the previously labeled training data. That is, the new labeled training dataset accounts for 25% + 75%∗k% of the entire original training dataset. For example, if the value of k% was set to 50%, we saved 37.5% (i.e., 1 − (25% + 75%∗50%)) annotation cost.(5)
A pretrained CNN (i.e., AlexNet, VGG16, ResNet50, or se_ResNet50) was fine-tuned on the new labeled training dataset and consequently evaluated on the test dataset.Figure 9
Workflow of the proposed framework.


### 2.5. Evaluation Metrics

Since the identification of vaginitis in microscopic leucorrhea images is a binary classification problem, we assessed the proposed framework on the following commonly used evaluation metrics in classification.Accuracy is the ratio of correct predictions to the total number of predictions. The accuracy equation is given by(4)accuracy=TP+TNTP+FP+TN+FN,where TP, FP, TN, and FN indicate true positive, false positive, true negative, and false negative, respectively.Precision is the ratio of the number of correctly predicted positive samples to the total number of predicted positive samples. The precision equation is given by(5)precision=TPTP+FP.Recall is the ratio of the number of correctly predicted positive samples to the total number of actual positive samples. The recall equation is given by(6)recall=TPTP+FN.F1 score is the harmonic mean of the precision and recall, which takes both false positive samples and false negative samples into account. The F1 score equation is given by(7)F1score=2×precision×recallprecision+recall.The Receiver Operating Characteristic (ROC) curve shows the trade-off between sensitivity and specificity, which measures the classifier performance under various threshold settings. Area under ROC curve (AUC) is a powerful metric to evaluate the performance of a binary classification model.


## 2.1. Dataset

The microscopic leucorrhea image dataset in this work was collected from the Sixth People’s Hospital of Chengdu, Sichuan Province. There were 229 female patients. All patients in this study had signed informed consent, and the study had been approved by the Medical Ethics Committee of the hospital.The microscopic leucorrhea images were obtained by a CX31 biological microscope (Olympus, Tokyo, Japan) and an EXCCD01400KMA CCD camera (Motic, Xiamen, China). The objective lens was 40x. The pixel size and the exposure time were set to 6.45μm × 6.45 µm and 40 ms, respectively. The field of view (FOV) in this optical system was 0.41 mm × 0.26 mm.There are a total of 1302 microscopic images with a resolution of 1,920 × 1,200 pixels. An experienced pathologist and a gynecologist manually annotated each image. Every normal image was labeled as negative 0, and images with vaginitis were labeled as positive 1. In the dataset, there are 569 positive samples and 733 negative samples. The normal leucorrhea image and the leucorrhea images with three common types of infectious vaginitis are shown in Figure1. We randomly selected 20% of the images as the test dataset (260 images) and the rest as the training dataset (1042 images).Figure 1
Different types of microscopic leucorrhea images. (a) Normal, negative sample. (b) Bacterial vaginosis, positive sample. (c) Candidal vaginitis, positive sample. (d) Trichomonas vaginitis, positive sample.
(a)(b)(c)(d)


## 2.2. Transfer Learning and CNN Architectures

Transfer learning is a data-effective technique for CNN training. It can save both computational resources and training period by transferring the learned knowledge from the massive annotated source dataset to the target dataset. In this work, we employed fine-tuning strategy that adopts the weights and biases from a CNN pretrained on ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) dataset [27] and then carried on a conventional training process on the target dataset. Different network structures vary in layer depth, feature extraction, and representation capabilities, which may result in different outcomes and trends for vaginitis classification. In this research, we opted for four popular and powerful state-of-the-art CNN architectures coupled with transfer learning. Based on the purpose of vaginitis recognition, the last layer of each CNN was replaced with an output layer with two neurons. We employed fine-tuning strategy in all layers instead of several layers because our target microscopic image dataset has very different characteristics from the ImageNet dataset. For the image preprocessing stage, we employed the bilinear interpolation transformation method to resize each microscopic image to 224 × 224 pixels before classification by the CNNs. The purpose of resizing the images is to fit the pretrained model input while saving computation resources and training time. Next, the details of the architectures of AlexNet, VGG16, ResNet50, and se_ResNet50 are described.AlexNet [25] won the ILSVRC competition in 2012 and has revolutionized the field of computer vision. As shown in Figure 2, AlexNet is a relatively shallow network, which contains five convolutional layers, three max-pooling layers, and three fully connected layers. The input image size was 224 × 224. The activation function followed by the convolution layer was Rectified Linear Unit (ReLU), and the Softmax activation function was used before output.Figure 2
AlexNet architecture.VGG [28] was proposed in 2014, and it is the first time deep learning achieved top-5 error under 10% in ILSVRC competition. VGG has a very deep network design, and the kernel size is very small (3 × 3), which decreases the number of parameters while maintaining performance. For example, the receptive field size of two layers of 3 × 3 kernel is equal to that of a single layer of 5 × 5 kernel; however, the number of parameters is cut by 28%. As presented in Figure 3, VGG16 consists of 16 layers that have weights (i.e., 13 convolutional layers and 3 fully connected layers) and 5 max-pooling layers.Figure 3
VGG16 architecture.He et al. [29] empirically showed that deeper neural network does not mean stronger performance due to overfitting and vanishing gradient problem. Therefore, they proposed the residual block to alleviate the problem. Figure 4 indicates the structure of the residual block. The identity mapping is adding the input x to the output F(x) of the multiple convolutional layers block as the final output. The skip connection considers x + F(x) to be the input to the next layer. As illustrated in Figure 5, ResNet50 stacks the residual blocks to construct the deep CNN network, and the skip connections between layers are indicated in blue arrow lines. The residual block and skip connections enable the ResNet to become extremely deep and have empirically shown better performance than previous deep neural networks in ImageNet classification.Figure 4
Residual block.Figure 5
ResNet50 architecture.Hu et al. [30] proposed a squeeze and excitation (SE) block, which improved the CNN performance and won the 2017 ILSVRC competition. The detailed structure of the SE block is shown in Figure 6. The SE block contains two steps: squeeze and excitation. The squeeze step transforms every channel of the input feature map into a single numeric value by global pooling. The excitation step uses two fully connected layers to add the necessary nonlinearity and then uses the output as the weight matrix of each channel to scale the original feature map input. The SE block significantly boosts network performance by helping the network adjust the weights of each feature map. This block can be conveniently added to any existing structure without causing an extra computational burden. As indicated in Figure 7, se_ResNet50 ensembles the SE block into the residual block on the basis of the structure of ResNet50.Figure 6
Squeeze and excitation block.Figure 7
se_ResNet50 architecture.


## 2.3. Active Learning for Informative Sample Selection

Some training examples make the CNN exhibits superior performance compared with other training data. Active learning is a commonly used technique, which explores the minimal subset of the training cohort that enables the neural network to maintain high performance. In this work, we used entropy and Kullback–Leibler (KL) divergence as metrics to compute the uncertainty score of every unlabeled image and then select the most informative samples based on the obtained uncertainty scores.As shown in Figure8, for every unlabeled image X in the training dataset, four fine-tuned CNNs (i.e., AlexNet, VGG16, ResNet50, and se_ResNet50) were employed to obtain the predicted probabilities. Note that the fine-tuned models only perform the inference process instead of the training process. Assuming that Pi and Pj are obtained probabilities from different fine-tuned CNNs, the entropy formula of the image X is given by (1)HX=−∑iPilogPi.Figure 8
Workflow of uncertainty score calculation.The KL divergence formula is given by(2)DKLPiPj=∑i∑jPilogPiPj.The uncertainty score of an unlabeled image is the sum of entropy and KL divergence, and we calculated the uncertainty score for every image in unlabeled training data to obtain the uncertainty score list. The uncertainty score formula is given by(3)USX=αHX+βDKLX.According to the best experimental results, the coefficientsα and β were set to 1. Since there are 4 different probabilities that were obtained in the proposed framework, every unlabeled image has 4 entropy scores and 12 KL divergence scores. The higher the uncertainty score, the more informative the sample. We sorted the uncertainty score list and selected the most informative samples.


## 2.4. Workflow of the Proposed Framework

In this work, we propose a novel data-efficient framework for the identification of vaginitis based on deep learning. The high-quality annotation for microscopic data is extremely time-consuming and requires a huge amount of budget. To address this problem, the proposed framework integrates transfer learning and active learning techniques to achieve competitive CNN performance at the minimum annotation cost. As depicted in Figure9, the workflow is divided into 5 steps:(1)
We randomly selected and labeled 25% of samples from the completed training cohort. The labeled training subset was used to fine-tune the pretrained AlexNet, VGG16, ResNet50, and se_ResNet.(2)
The unlabeled 75% (i.e., 1–25%) training data was input to the four fine-tuned CNNs to obtain the predicted probabilities. Note that the fine-tuned models only perform the inference process to calculate outputs without backpropagation.(3)
We used the obtained probability list to calculate the uncertainty score (i.e., entropy and KL divergence), which measures the informativeness of each unlabeled training sample. Then, we selected k% of the unlabeled training data as the most informative samples according to the sorted uncertainty score list.(4)
We annotated the selected informative samples and added them to the previously labeled training data. That is, the new labeled training dataset accounts for 25% + 75%∗k% of the entire original training dataset. For example, if the value of k% was set to 50%, we saved 37.5% (i.e., 1 − (25% + 75%∗50%)) annotation cost.(5)
A pretrained CNN (i.e., AlexNet, VGG16, ResNet50, or se_ResNet50) was fine-tuned on the new labeled training dataset and consequently evaluated on the test dataset.Figure 9
Workflow of the proposed framework.


## 2.5. Evaluation Metrics

Since the identification of vaginitis in microscopic leucorrhea images is a binary classification problem, we assessed the proposed framework on the following commonly used evaluation metrics in classification.Accuracy is the ratio of correct predictions to the total number of predictions. The accuracy equation is given by(4)accuracy=TP+TNTP+FP+TN+FN,where TP, FP, TN, and FN indicate true positive, false positive, true negative, and false negative, respectively.Precision is the ratio of the number of correctly predicted positive samples to the total number of predicted positive samples. The precision equation is given by(5)precision=TPTP+FP.Recall is the ratio of the number of correctly predicted positive samples to the total number of actual positive samples. The recall equation is given by(6)recall=TPTP+FN.F1 score is the harmonic mean of the precision and recall, which takes both false positive samples and false negative samples into account. The F1 score equation is given by(7)F1score=2×precision×recallprecision+recall.The Receiver Operating Characteristic (ROC) curve shows the trade-off between sensitivity and specificity, which measures the classifier performance under various threshold settings. Area under ROC curve (AUC) is a powerful metric to evaluate the performance of a binary classification model.


## 3. Results

The hardware for the experiments was a NVIDIA GeForce RTX 2070 platform with 16 GB internal storage. The software environment was Python 3.8 and PyTorch 1.9.1. We conducted every single experiment 5 times to remove the effects that might appear by chance. The results were the average of 5 runs and presented along with the 95% confidence interval (CI). The batch size was set to 8. The stochastic gradient descent (SGD) optimizer with a momentum of 0.9 was used. The learning rate was set to 0.01 and decayed the learning rate by 0.975 every epoch. The epoch number was set to 10 for shortening the training process. The average running time is 3.78 ms per image when using the proposed method.


### 3.1. Comparison of the Proposed Informative Sample Selection Method and the Random Sampling Method

The proposed informative sample selection method selected and labeledk% of the most informative samples from the unlabeled training data and then added them to the prelabeled training dataset. The newly created subset was employed to fine-tune a pretrained CNN. In order to select the best proportion k% of the selected samples, we varied k% from the range of 10%–90% in a step of 10%. We also compared the results of the random sampling method that was randomly selected from the unlabeled training cohort. The baseline was obtained when we did not perform any selection method; that is, we annotated all the unlabeled training dataset (i.e., k% = 100%) and performed a conventional CNN fine-tuning process. We used the AlexNet as the pretrained CNN to obtain the test results.As it can be seen in Figure10, the orange line indicates the accuracy and AUC results of the proposed informative sample selection method, the blue line describes the results of the random sampling method, and the dotted gray line is the baseline that shows the results that labeled all training samples. The proposed sampling method outperforms the random sampling method at almost all the range of proportion k%, which demonstrates that the high performance of the proposed method is not the effect of random bias or noise. When k% was set to 50%, the accuracy result of the proposed method was closed to the baseline accuracy, and the AUC result surpassed the baseline AUC. Therefore, we set the value of k% to 50% in the following experiments.Figure 10
Comparison of the results of the proposed informative sample selection method and the random sampling method under different proportion of the selected samples. (a) Comparison of accuracy results. (b) Comparison of AUC results.
(a)(b)


### 3.2. Comparison of the Proposed Informative Sample Selection Method and the All Training Samples Selection Approach

Active learning is an approach that selects a minimal subset from the entire training cohort and is expected to achieve equally good or better results than using the complete training data. After performing the proposed informative sample selection algorithm, we fine-tuned different pretrained CNN architectures on the selected subset. Table1 lists the precision, recall, F1 score, accuracy, and AUC results of the proposed informative sample selection method and compares them with the results of the all training samples selection approach (i.e., using all training cohort instead of selecting a subset) on the AlexNet, VGG16, ResNet50, and Se_ResNet50 architecture.Table 1
Results of the informative sample selection method and the all samples selection approach under different pretrained CNNs.
CNN typeAnnotation methodPrecision (%)Recall (%)F1 score (%)Accuracy (%)AUC (%)AlexNetAll samples94.35 ± 2.6689.12% ± 2.1691.62 ± 1.0192.85 ± 0.9197.75% ± 0.59Informative samples93.05 ± 2.7190.18% ± 2.4691.54 ± 1.2992.69 ± 1.1697.88 ± 0.26VGG16All samples91.49 ± 1.4990.18 ± 1.1290.81 ± 0.5192.00 ± 0.4998.15 ± 0.36Informative samples92.03 ± 1.1391.05 ± 0.3891.54 ± 0.6092.62 ± 0.5697.25 ± 0.26ResNet50All samples95.29 ± 2.6489.47 ± 5.7392.16 ± 2.5793.38 ± 2.0498.86 ± 0.56Informative samples94.09 ± 3.3495.09 ± 2.2494.52 ± 0.8795.15 ± 0.8798.93 ± 0.31Se_ResNet50All samples96.06 ± 1.5493.51 ± 0.9894.76 ± 0.7595.46 ± 0.6799.06 ± 0.16Informative samples96.99 ± 0.7590.18 ± 2.4693.44 ± 1.3695.06 ± 1.0998.94 ± 0.28Figure11 illustrates the content of Table 1. The blue bar describes the results when the pretrained CNN was fine-tuned on the selected informative subset, and the orange bar shows the results of labeling the entire training cohort to fine-tune the pretrained neural network. As Table 1 and Figure 11 indicate, overall, the proposed informative sample selection method achieves similar or better performance than annotating all training samples approach for each CNN architecture. The proposed method performed best when the pretrained CNN was ResNet50. The pretrained se_ResNet50 achieved slightly worse results when coupled with the proposed method, only obtaining a better result in terms of precision metric.Figure 11
Results of the informative sample selection method and the all training samples selection approach under different pretrained CNNs. (a) AlexNet; (b) VGG16; (c) ResNet50; (d) se_ResNet50.
(a)(b)(c)(d)Figure12 presents the confusion matrix and the ROC curve generated by the pretrained model ResNet50, which showed the best performance using the proposed informative sample selection method.Figure 12
The confusion matrix and ROC curve of the model that showed the best performance. (a) Confusion matrix. (b) ROC curve.
(a)(b)


### 3.3. Comparison of Employing Different CNNs and a Single Type of CNN in the Proposed Informative Sample Selection Method

Since different network structures have different kernel sizes, layer depth, and building blocks, their feature extraction abilities and classification capabilities may also be different. Therefore, four CNNs with large variations in architecture were employed in this work, and we assume that this CNN variability helps select the subset with better training value. To prove the effectiveness of using different CNNs, we compared the experimental results using ResNet50 as the single type CNN for calculating the uncertainty score list for unlabeled training examples. We selected ResNet50 because it was the best performance CNN according to Section3.2. Instead of using four CNNs, only the pretrained ResNet50 was fine-tuned on the prelabeled training data. We changed the learning rate to 0.005, 0.008, 0.01, and 0.015 and repeated the fine-tuning process. The four fine-tuned ResNet50 models were obtained, and we used them to finish the subsequent informative sample selection process as described in Section 2.3. The pretrained ResNet50 was employed to be fine-tuned on the selected subset and then perform classification. For comparison, we also present the results without using any informative sample selection technique; that is, we labeled whole training data and fine-tuned a pretrained ResNet50 model for vaginitis classification.As it can be seen in Figure13, the blue bar shows the results of the informative sample selection method using only a single type CNN, the orange bar describes the results of the proposed method in this study, and the gray bar indicates the results of annotating all training data without informative selection. The proposed method outperforms the other two approaches almost in all tested metrics except for the precision metric; however, the precision results did not differ substantially. When comparing the blue bar with the gray bar, the informative sample selection method of using only one type of CNN performs only slightly worse, yet it is still effective considering saving a huge amount of annotation cost.Figure 13
Comparison of the results of using a single type of CNN in the informative sample selection method, the results of using different types of CNNs in informative sample selection, and the results without informative samples selection.


## 3.1. Comparison of the Proposed Informative Sample Selection Method and the Random Sampling Method

The proposed informative sample selection method selected and labeledk% of the most informative samples from the unlabeled training data and then added them to the prelabeled training dataset. The newly created subset was employed to fine-tune a pretrained CNN. In order to select the best proportion k% of the selected samples, we varied k% from the range of 10%–90% in a step of 10%. We also compared the results of the random sampling method that was randomly selected from the unlabeled training cohort. The baseline was obtained when we did not perform any selection method; that is, we annotated all the unlabeled training dataset (i.e., k% = 100%) and performed a conventional CNN fine-tuning process. We used the AlexNet as the pretrained CNN to obtain the test results.As it can be seen in Figure10, the orange line indicates the accuracy and AUC results of the proposed informative sample selection method, the blue line describes the results of the random sampling method, and the dotted gray line is the baseline that shows the results that labeled all training samples. The proposed sampling method outperforms the random sampling method at almost all the range of proportion k%, which demonstrates that the high performance of the proposed method is not the effect of random bias or noise. When k% was set to 50%, the accuracy result of the proposed method was closed to the baseline accuracy, and the AUC result surpassed the baseline AUC. Therefore, we set the value of k% to 50% in the following experiments.Figure 10
Comparison of the results of the proposed informative sample selection method and the random sampling method under different proportion of the selected samples. (a) Comparison of accuracy results. (b) Comparison of AUC results.
(a)(b)


## 3.2. Comparison of the Proposed Informative Sample Selection Method and the All Training Samples Selection Approach

Active learning is an approach that selects a minimal subset from the entire training cohort and is expected to achieve equally good or better results than using the complete training data. After performing the proposed informative sample selection algorithm, we fine-tuned different pretrained CNN architectures on the selected subset. Table1 lists the precision, recall, F1 score, accuracy, and AUC results of the proposed informative sample selection method and compares them with the results of the all training samples selection approach (i.e., using all training cohort instead of selecting a subset) on the AlexNet, VGG16, ResNet50, and Se_ResNet50 architecture.Table 1
Results of the informative sample selection method and the all samples selection approach under different pretrained CNNs.
CNN typeAnnotation methodPrecision (%)Recall (%)F1 score (%)Accuracy (%)AUC (%)AlexNetAll samples94.35 ± 2.6689.12% ± 2.1691.62 ± 1.0192.85 ± 0.9197.75% ± 0.59Informative samples93.05 ± 2.7190.18% ± 2.4691.54 ± 1.2992.69 ± 1.1697.88 ± 0.26VGG16All samples91.49 ± 1.4990.18 ± 1.1290.81 ± 0.5192.00 ± 0.4998.15 ± 0.36Informative samples92.03 ± 1.1391.05 ± 0.3891.54 ± 0.6092.62 ± 0.5697.25 ± 0.26ResNet50All samples95.29 ± 2.6489.47 ± 5.7392.16 ± 2.5793.38 ± 2.0498.86 ± 0.56Informative samples94.09 ± 3.3495.09 ± 2.2494.52 ± 0.8795.15 ± 0.8798.93 ± 0.31Se_ResNet50All samples96.06 ± 1.5493.51 ± 0.9894.76 ± 0.7595.46 ± 0.6799.06 ± 0.16Informative samples96.99 ± 0.7590.18 ± 2.4693.44 ± 1.3695.06 ± 1.0998.94 ± 0.28Figure11 illustrates the content of Table 1. The blue bar describes the results when the pretrained CNN was fine-tuned on the selected informative subset, and the orange bar shows the results of labeling the entire training cohort to fine-tune the pretrained neural network. As Table 1 and Figure 11 indicate, overall, the proposed informative sample selection method achieves similar or better performance than annotating all training samples approach for each CNN architecture. The proposed method performed best when the pretrained CNN was ResNet50. The pretrained se_ResNet50 achieved slightly worse results when coupled with the proposed method, only obtaining a better result in terms of precision metric.Figure 11
Results of the informative sample selection method and the all training samples selection approach under different pretrained CNNs. (a) AlexNet; (b) VGG16; (c) ResNet50; (d) se_ResNet50.
(a)(b)(c)(d)Figure12 presents the confusion matrix and the ROC curve generated by the pretrained model ResNet50, which showed the best performance using the proposed informative sample selection method.Figure 12
The confusion matrix and ROC curve of the model that showed the best performance. (a) Confusion matrix. (b) ROC curve.
(a)(b)


## 3.3. Comparison of Employing Different CNNs and a Single Type of CNN in the Proposed Informative Sample Selection Method

Since different network structures have different kernel sizes, layer depth, and building blocks, their feature extraction abilities and classification capabilities may also be different. Therefore, four CNNs with large variations in architecture were employed in this work, and we assume that this CNN variability helps select the subset with better training value. To prove the effectiveness of using different CNNs, we compared the experimental results using ResNet50 as the single type CNN for calculating the uncertainty score list for unlabeled training examples. We selected ResNet50 because it was the best performance CNN according to Section3.2. Instead of using four CNNs, only the pretrained ResNet50 was fine-tuned on the prelabeled training data. We changed the learning rate to 0.005, 0.008, 0.01, and 0.015 and repeated the fine-tuning process. The four fine-tuned ResNet50 models were obtained, and we used them to finish the subsequent informative sample selection process as described in Section 2.3. The pretrained ResNet50 was employed to be fine-tuned on the selected subset and then perform classification. For comparison, we also present the results without using any informative sample selection technique; that is, we labeled whole training data and fine-tuned a pretrained ResNet50 model for vaginitis classification.As it can be seen in Figure13, the blue bar shows the results of the informative sample selection method using only a single type CNN, the orange bar describes the results of the proposed method in this study, and the gray bar indicates the results of annotating all training data without informative selection. The proposed method outperforms the other two approaches almost in all tested metrics except for the precision metric; however, the precision results did not differ substantially. When comparing the blue bar with the gray bar, the informative sample selection method of using only one type of CNN performs only slightly worse, yet it is still effective considering saving a huge amount of annotation cost.Figure 13
Comparison of the results of using a single type of CNN in the informative sample selection method, the results of using different types of CNNs in informative sample selection, and the results without informative samples selection.


## 4. Discussion

Vaginitis is a prevalent gynecological disease that not only affects the health and life quality of females but also increases the potential risks of other severe illnesses. For rapid and reliable early diagnosis of vaginitis, we propose a data-efficient framework for the identification of vaginitis based on deep learning in this work. While deep learning outperforms the traditional image processing and machine learning methods on both speed and accuracy, it has a drawback that it requires massive of well-annotated samples to train a high-performance model. The proposed framework combines the advantages of transfer learning and active learning techniques to effectively save substantial annotation costs. To explore the best proportion of the selected subset for the proposed method, we varied the proportion from 10% to 90%, and the experiment results show that 50% is the most appropriate proportion value in this vaginitis classification task. We also compared the results of the proposed method with the random sampling approach, which demonstrates that the high performance was not due to random bias or noise. Next, we performed the proposed method on different kinds of CNN architecture, and they all achieved competitive performance while saving 37.5% labeling cost. As Table1 and Figure 11 indicate, the best performing model was ResNet50, which achieved 94.09% ± 3.34% precision, 95.09% ± 2.24% recall, 94.52% ± 0.87% F1 score, 95.15% ± 0.87% accuracy, and 98.93% ± 0.31% AUC.In contrast to the previous work [19], which presented a transfer learning-based active learning framework for brain tumor classification, we employed different types of CNN architectures in the informative selection methods, and [19] only used the shallow network AlexNet in their sample uncertainty sampling approach. We assume that the variations in layer depth, convolutional filter size, and building block structure of CNNs help the informative sample selection process. As shown in Figure 13, the experimental results in this study confirm our hypothesis. Besides, our evaluation method can be regarded as more objective, scientific, and reasonable because the proposed method used different metrics (i.e., precision, recall, F1 score, accuracy, and AUC) instead of a single AUC metric. Wang et al. [11] also presented an automatic framework for morphologic classification and diagnosis of bacterial vaginosis, and they trained their customized network from scratch instead of employing a transfer learning method, which requires a huge amount of annotated training data (23,280 samples). Another superiority of our presented method was also seen in the fact that it identified three types of vaginitis (i.e., bacterial vaginosis, candidal vaginitis, and trichomonas vaginitis) instead of only bacterial vaginosis recognition in [11].The present experiments have demonstrated that the proposed framework is a valid and useful approach for the identification of vaginitis while saving considerable annotation costs. One limitation of the current study is that it is still unclear how the structure of CNN affects the performance of the proposed framework. As part of future research, we intend to experimentally investigate the exact effect of different CNN architecture on the proposed pipeline. Furthermore, we also plan to optimize the proposed framework by exploring other annotation reduction methods for microscopic imaging, for instance, data augmentation technique, metalearning strategy, and few-shot learning method.


## 5. Conclusions

Vaginitis is a prevalent gynecological disorder affecting millions of women all over the world. We have established a data-efficient framework for the identification of vaginitis based on deep learning, which can aid rapid and reliable early diagnosis of vaginitis. The proposed pipeline presented a novel informative sample selection method for microscopic images by integrating transfer learning and active learning, which saved 37.5% annotation cost while maintaining competitive performance. In addition, the proposed pipeline can be extended to other microscopic imaging applications to address the issue of high annotation cost and limited medical data.



---

*Source: 1929371-2022-02-27.xml*