Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial‐aware booster

IET Image ProcessingVolume 14, Issue 17 p. 4717-4725 Research ArticleFree Access Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial-aware booster Dongyang Ma, Dongyang Ma School of Software, Shandong University, Jinan, 250101 People's Republic of ChinaSearch for more papers by this authorJinhua Liu, Jinhua Liu School of Software, Shandong University, Jinan, 250101 People's Republic of ChinaSearch for more papers by this authorJing Li, Jing Li Zibo Central Hospital, Shandong University, Zibo, 255000 People's Republic of ChinaSearch for more papers by this authorYuanfeng Zhou, Corresponding Author Yuanfeng Zhou yfzhou@sdu.edu.cn School of Software, Shandong University, Jinan, 250101 People's Republic of ChinaSearch for more papers by this author Dongyang Ma, Dongyang Ma School of Software, Shandong University, Jinan, 250101 People's Republic of ChinaSearch for more papers by this authorJinhua Liu, Jinhua Liu School of Software, Shandong University, Jinan, 250101 People's Republic of ChinaSearch for more papers by this authorJing Li, Jing Li Zibo Central Hospital, Shandong University, Zibo, 255000 People's Republic of ChinaSearch for more papers by this authorYuanfeng Zhou, Corresponding Author Yuanfeng Zhou yfzhou@sdu.edu.cn School of Software, Shandong University, Jinan, 250101 People's Republic of ChinaSearch for more papers by this author First published: 25 February 2021 https://doi.org/10.1049/iet-ipr.2020.0688Citations: 1AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract With the development of artificial intelligence and image processing technology, more and more intelligent diagnosis technologies are used in cervical cancer screening. Among them, the detection of cervical lesions by thin liquid-based cytology is the most common method for cervical cancer screening. At present, most cervical cancer detection algorithms use the object detection technology of natural images, and often only minor modifications are made while ignoring the specificity of the complex application scenario of cervical lesions detection in cervical smear images. In this study, the authors combine the domain knowledge of cervical cancer detection and the characteristics of pathological cells to design a network and propose a booster for cervical cancer detection (CCDB). The booster mainly consists of two components: the refinement module and the spatial-aware module. The characteristics of cancer cells are fully considered in the booster, and the booster is light and transplantable. As far as the authors know, they are the first to design a CCDB according to the characteristics of cervical cancer cells. Compared with baseline (Retinanet), the sensitivity at four false positives per image and average precision of the proposed method are improved by 2.79 and 7.2%, respectively. 1 Introduction Cervical cancer is the most common gynecological malignancy, with the fourth-highest morbidity and mortality [1]. However, cervical cancer is not only cancer with a clear cause but also cancer that can be cured and prevented. If it can be detected early, the incidence of this disease will be greatly reduced. In recent decades, cervical cytology screening [2] combined with human papillomavirus (HPV) detection has been widely applied, so that cervical cancer and precancerous lesions can be detected and treated early. The morbidity and mortality of cervical cancer have been significantly reduced. If the patient is infected with the HPV virus, the cervical epithelial cells and squamous epithelial cells will appear various degrees of morphological changes, such as nucleus enlargement, hyperchromasia or deeper staining nuclear and curved nuclear boundaries. According to the characteristics of diseased cells, thin liquid-based cytology (LBC) can significantly improve the accuracy of cervical cancer detection and has become one of the essential means of cervical cancer diagnosis and prevention. Thin LBC is also facing some challenges, such as high workload, lack of professional cytopathologists, slow reading under a microscope and low accuracy of cervical cancer detection caused by the level difference of cytopathologists. The detection process of cervical cancer is complex and expensive, which makes the prevalence of cervical cancer screening in underdeveloped areas low, and the mortality rate of cervical cancer very high [3, 4]. In order to reduce the workload of pathologists and improve the efficiency and accuracy of cervical cancer detection, some computer-aided diagnosis technologies have been proposed [5-7]. The traditional detection methods of pathological cells can be divided into two steps: first, the cells (cytoplasm and nucleus) are segmented; second, the characteristics of cells are extracted, and the abnormal cells are classified. A good result of cell segmentation plays an important role in extracting cell features and then affects the classification results. At present, there are many works in this field [8-20]. Due to cell overlapping, cell clusters and abnormal cell nuclei, it is difficult to segment each cell accurately. In order to solve the problem of overlap, [21-26] were proposed. After getting good segmentation results, we need to extract morphological and structural features of the nucleus and cytoplasm, and then use a classifier to classify these features to get pathological cells [27-30]. Due to the inefficiency of manual feature extraction, it is difficult to improve the performance of classification. Deep learning, especially the development of convolutional neural networks (CNNs), provides help to solve this problem. These methods use a convolution neural network to extract deep features and classify cells [31-33]. This also makes researchers realise that feature extraction plays a vital role in cervical cancer detection. With the development of artificial intelligence and deep learning technology, some methods begin to consider the use of deep learning methods for cervical cancer detection [34-36]. Due to the advantages of CNNs in feature extraction, these methods based on deep learning show better performance than traditional methods. However, most of these deep learning based methods do not start from the characteristics of cervical cancer cells when designing the network structure. Some methods even directly use some excellent object detection methods of natural images in the detection of cervical cancer cells. Other methods modify the object detection methods, but the improvement of these structures does not start from the needs of cervical cancer detection task. Although these methods can get good detection results, the network has poor interpretability. Based on the above analysis, we propose a network architecture for cervical cancer detection in cervical smear images. The network is based on feature pyramid network (FPN) [37], which can solve the problem of multi-scale in object detection, and improve the performance of small object detection without increasing the parameters. Besides, we analyse the requirements of cervical cancer detection task for feature extraction and design a lightweight booster. The booster consists of two components: the refinement module (RM) and the spatial-aware module (SM). By analysing the differences between cancerous cells and normal cells in cervical smear images, we can find that there are many subtle differences between cancerous cells and normal cells. For example, the boundaries of nuclei of cancerous cells are not smooth, the nuclei are stained deeply, and there are many impurities in abnormal regions. These detailed features are essential criteria to judge whether the cells are cancerous or not. In order to make better use of the detail features of cervical smear images, we designed the RM. Besides, to judge whether a cell is cancerous or not, we need to refer to the spatial context information of the cell. For example, if the cell with a larger and deeper staining nucleus is more likely considered to be cancerous. For this reason, we design the SM for cervical cancer detection. Our contributions are as follows: At present, there are few network architectures designed specifically for the specific task of cervical cancer detection. We design a booster for the task of cervical cancer detection, which can improve the accuracy of cervical cancer detection. As far as we know, we are the first to design a booster according to the characteristics of cervical cancer cells in cervical smear images. This booster can be added to the detector with FPN as the backbone. What is important is that the idea of focusing on the details of features and global information proposed in this work has been proved to be feasible. In the future, these ideas can be used to design or improve other networks for cervical cancer detection. We verify the advantages of our method in cervical cancer detection through experiments. In order to verify the effectiveness of our proposed booster, we also add the booster to other object detection methods, thereby improving the detection sensitivity and AP. 2 Related work Abnormal region detection in cervical smear images plays an increasingly important role in clinical applications such as computer-aided diagnosis. With the rapid development of modern CNNs, object detection on natural images has achieved excellent performance. So far, modern object detection techniques on natural images are mainly divided into two categories, proposal-driven two-stage detector and proposal-free single stage detector. For two-stage detectors, such as Faster R-CNN series [38-40], Cascade R-CNN [41], R-FCN [42] and FPN [37], the algorithm process needs to be completed in two steps, first obtaining sparse candidate proposals, and then further classifying these candidate proposals and refining the coordinates. In contrast, single-stage detectors, such as SSD series [43, 44], YOLO series [45-47] and RetinaNet [48], do not need to obtain candidate proposals, but obtain classification scores and regression coordinate values in one operation. Most recently, new anchor-free single-stage detectors, such as CornerNet [49], FcosNet [50], FoveaBox [51] and FreeAnchor [52], have abandoned the anchor mechanism of previous detectors and achieved promising performance. In recent years, object detection has also played an important role in medical images, such as lung nodules [53], skin lesions [54], liver lesions [55] and so on. More recently, unified lesion detection [56, 57] has also been achieved outstanding results on the DeepLesion dataset [58] containing multiple lesion types. However, the method of computer-assisted screening for cervical cancer, the most common malignant tumour of gynaecology, is often divided into two steps in the past: segmentation [17, 59] and classification [32, 33]. After the entire image is segmented, the small pieces are classified into abnormal and normal ones, and finally used for the diagnosis of cervical cancer. Until recent years, the work of detecting abnormal areas in cervical smear images has begun to emerge. Meiquan et al. [34] used object detection technology based on Faster R-CNN [40] to achieve object-level detection for five types of abnormalities (ASC-US, LSIL, HSIL, EC and MSC) in cervical smear images. Their conclusion proves that the accurate detection and classification of various abnormal cells and the statistics of various abnormal cells through computer technology can draw a more comprehensive conclusion, thus illustrating the object detection technology can be effectively applied to the diagnosis of cervical cancer. A novel abnormal region detection method for cervical screening in LBC images based on the R-FCN [42] was proposed in [35]. It also defined a new measurement method hit degree to describe the degree to which each detection area matches the corresponding ground truth. Zhang et al. [36] used both single-stage RetinaNet [48] and two-stage Faster R-CNN [40] to classify and locate six types of abnormalities (ASC-US, LSIL, ASC-H, HSIL, SCC and AGC) in cervical smear images. More importantly, it released the largest cervical cytology dataset named Deep Cervical Cytological Lesions to facilitate future computer-assisted cervical cancer screening. 3 Method Referring to the modular idea of mmdetection [60], our single-stage object detector for cervical cancer detection can be divided into four components: Backbone, Neck, booster for cervical cancer detection (CCDB) and DenseHead. Although there are many possible options for the details of these components, we take RetinaNet's design for simplicity and fair comparison. ResNet-50 [61] is used for the Backbone, FPN is used for the Neck and ReinaHead is used for the DenseHead (subnetworks). As shown in Table 1, our experiment shows that the combination of these components can achieve better performance in cervical cancer detection. Fig. 1 shows the framework of our detector. Fig. 2 shows the details of the proposed network. Fig. 1Open in figure viewerPowerPoint Framework of single-stage detector and our single-stage detector for cervical cancer detection Fig. 2Open in figure viewerPowerPoint Frameworks of the proposed approach. The first row is the general framework of the proposed approach. The detailed architecture of the CCDB module is shown in the second row Table 1. Ablation experiments of CCDB were evaluated by AP Method Backbone Neck DenseHead Image size Param. AP (%) Faster R-CNN [40] Resnet-50 FPN RPNHead 800*800 41.13M 57.1 Faster R-CNN+CCDB Resnet-50 FPN RPNHead 800*800 41.13M 65.3 (+8.2) Cascade R-CNN [41] Resnet-50 FPN RPNHead 800*800 68.93M 56.6 Cascade R-CNN+CCDB Resnet-50 FPN RPNHead 800*800 68.93M 63 (+6.4) FreeAnchor [52] Resnet-50 FPN FreeAnchorRetinaHead 800*800 36.1M 54.8 FreeAnchor+CCDB Resnet-50 FPN FreeAnchorRetinaHead 800*800 36.1M 61.7 (+7.2) FoveaBox [51] Resnet-50 FPN FoveaHead 800*800 37.2M 57.4 FoveaBox+CCDB Resnet-50 FPN FoveaHead 800*800 37.2M 63.3 (+5.9) RetinaNet [48] Resnet-50 FPN RetinaHead 800*800 36.1M 58.1 RetinaNet+CCDB Resnet-50 FPN RetinaHead 800*800 36.1M 65.3 (+6.2) Bold indicates the AP value after adding the CCDB module. Our goal is to design a kind of network for cervical cancer detection, so we take FPN as the feature extraction network and design a lightweight CCDB. The design of the booster fully considers the network requirements of the cervical cancer detection task, and the booster is very lightweight and transplantable. The proposed booster mainly consists of two components: the RM and the SM. We will introduce FPN in Section 3.1, the RM in Section 3.2, the SM in Section 3.3 and subnetworks in Section 3.4. 3.1 Feature pyramid network backbone Following [37, 48], the proposed method uses a FPN backbone on top of a feedforward ResNet-50 architecture. Each level of FPN can detect objects of different scales, which improves the multi-scale predictions of full convolution networks. FPN network mainly consists of three components: bottom-up pathway, top-down pathway and lateral connections. The process of the bottom-up pathway is a common forward propagation process of the neural network. After convolution kernel calculation, the resolution of the feature map will be reduced, and the down-sampling factor is 2. For the ith down-sampled pyramid layer, we use C i D to represent it. In the top-down pathway, the more abstract and semantic high-level feature map is upsampled with an up-sampling factor of 2. For the ith up-sampled pyramid layer, we use C i U to represent it. The lateral connections merge the down-sampled feature maps and the up-sampled feature maps. The merged feature maps can be written as P i = C i D ⊕ C i U ,(1) where ⊕ is the element-wise addition operation and P i is the output of the ith level of FPN (The resolution of P i is 1 2 i of the input resolution). We use layers P 3 to P 7 to build the feature pyramid, and each pyramid level has 256 channels. For more details of FPN design, please refer to [37, 48]. 3.2 Refinement module As we have analysed before, there are many detail differences between cervical cancer cells and normal cells in the nuclear boundary and nuclear staining. In order to better perceive the detailed characteristics of cells, we designed the RM, as shown in Fig. 2. The RM is mainly designed based on the FPN network. The input of RM is the output P i of each layer of FPN and the lowest feature layer C 1 of the bottom-up pathway. We down-sample on C 1 with a down-sampling factor of 2. The result of the down-sampling is added to the output P i of each layer of the FPN. We define C 1 _ i D as the feature layer of C 1 after down-sampling. After the processing of the RM, feature maps can be written as R i = P i ⊕ C 1 _ i D = C i D ⊕ C i U ⊕ C 1 _ i D (2) We build RM with all R i RM = { R i } (3) After the operation of the RM, we can make better use of the detail characteristics of cervical cells in cervical smear images. It plays an important role in the detection of cervical cancer cells because the detection of cervical cancer cells should pay more attention to the details than other object detection tasks. In addition, the RM is mainly composed of the down-sampling and the element-wise addition, so it will not introduce too many parameters. 3.3 Spatial-aware module There are many differences between cervical cancer cells and normal cells, but these differences are difficult to be described by hand-extracted features, especially some high-level semantic information. In addition, to judge whether a cell is a cancerous cell, we need to refer to global information. Based on these considerations, we design a SM that can extract high-level semantic information and perceive global information. The SM is mainly composed of spatial group-wise enhance (SGE) [62] module. The SM performs the SGE operation on the output of each scale of the RM, so as to obtain the effective features of different scales, as shown in SM in Fig. 2. The SGE (as shown in SGE in Fig. 2) adjusts the importance of each sub-feature by generating an attention factor for each spatial position in each semantic group. Each group can enhance its learning expression and suppress the possible noise independently. Li et al. [62] add it to the feature extraction network based on Resnet and found that it is helpful to improve the performance of the network, especially to perceive advanced semantic information and extract global features. Instead of using it in the backbone, we used this module in our booster to handle different levels of RM's output R i . Firstly, the SGE divides the C-channels feature map into G-groups. The number of channels in each group is C G and the size of feature map is H × W. Taking a group as an example, each position can be represented by a vector, that is, Π = { x 1 … m } , x i ∈ R C G , m = H × W. In order to sense the global information, the semantic vectors of this group are approximately obtained by the following global average function: g = 1 m ∑ i = 1 m x i (4) We use g to generate importance coefficients c i for each feature. c i measures the similarity between global semantic feature g and local feature x i to some extent. c i = g ⋅ x i (5) Next, c i needs to be normalised c i ^ = c i − μ c σ c − ϵ , μ c = ∑ j m c j , σ c 2 = 1 m ∑ j m ( c j − μ c ) 2 (6) To scale and shift the normalised value, do the following: α i = γ c i ^ + β (7) Finally, the enhanced features are obtained x i ^ = x i ⋅ σ ( α i ) , (8) where σ is the Sigmod function. Through the calculation process of SGE, we can find that it can also realise the function of global attention mechanism. The process of SGE processing the feature map f can be defined as S f and the calculation process of SM can be written as S i = S f ( R i ) , (9) where S i represents the ith layer of SM and R i represents the ith layer of RM. The SM can be written as S M = { S i }(10) Each S i contains G groups. Li et al. [62] find that when the value of G in their dataset is 64, a better result can be achieved. As there is not much advanced semantic information in the cervical smear image, the SGE structure may not be suitable for our task. In order to make SGE suitable for our task, we test the value of G, as shown in Fig. 3. For our task, when G = 8, we can get better results. For other parameters ( ϵ , γ , β … ), we refer to [62]. Fig. 3Open in figure viewerPowerPoint Performance when SGE takes different G 3.4 Subnetworks and loss function As shown in e.g. (10), through the processing of CCDB, we can get the feature map S i of different scales. For the feature map of 256 channels ( S i ), we use the classification subnet and box regression subnet to classify and locate, as shown in Fig. 4. Classification subnet predicted the probability that A anchors belong to K classes. Box regression subnet regressed the offset between each anchor and the nearby ground truth. The details and parameters follow the settings in [48]. Fig. 4Open in figure viewerPowerPoint Subnetworks In the single-stage object detection algorithm, it often faces the problem of class imbalance. To solve this problem, focal loss [48] is proposed FL ( p t ) = − ( 1 − p t ) γ log ⁡ ( p t ) (11) Compared with the standard cross-entropy loss function, FL introduces a balance term − ( 1 − p t ) γ , which makes the accuracy of object detection greatly improved. For our task, the training loss is the sum of the focal loss and the standard smooth L 1 loss used for box regression [39]. 4 Experiments and discussion 4.1 Dataset and evaluation metrics 4.1.1 Dataset At present, there are mainly datasets for cervical cytology research [7, 28, 34, 36, 63-65], among which [28, 63-65] are primarily used for the segmentation and classification of cervical cells, [7, 34, 36] can be used for the detection of cervical pathological cells, but so far these datasets that can be used for detection have not been disclosed. Our data comes from the Alibaba Tianchi competition [66], which provides slid images of cervical smear labelled by professional doctors. Each slid image is obtained under a 20 times digital scanner; the resolution of each slide is about 40,000 × 40,000. The positions of abnormal squamous cells are marked on 500 positive slid images by professional doctors. The abnormal squamous cells mainly include ASC-US, LSIL, ASC-H and HSIL. We crop the 500 positive slid images. Cut each slid image into 2500 (50 ×50) images, and then screen the cut images. Only the images with abnormal squamous cells are retained. In this way, we obtain 4107 images, with 5414 abnormal area labels in total. We divide the 4107 images into a training set (68%), verification set (12%) and test set (20%). As shown in Fig. 5. Fig. 5Open in figure viewerPowerPoint Dataset 4.1.2 Evaluation metrics We use average precision (AP) and sensitivity to evaluate the performance of various methods. For AP calculation, we take IOU as 0.5. Sensitivity, also known as true positive rate, is a common evaluation method in pathological detection. In our experiments, we calculate the sensitivity at different false positives per image. For the calculation of sensitivity, we take the value of IOU as 0.5. Sensitivity can be written as Sensitivity = TP ( TP + FN ) , (12) where TP is true positive, and FN is false negative. 4.2 Implementation details Our experiments are carried out on Ubuntu, and the GPU used is GTX 1050 Ti. In order to make a fair comparison, our experiments are based on Pyorch [67] and mmdetection [60]. The images entered during the training are resized to a maximum scale of 800 × 800. We train the detector on a single GPU (2 pictures on one GPU), and we train 12 epochs in total. The initial value of the learning rate is 0.01, which will be reduced after 8 and 11 epochs, respectively, and the weight decay factor is 0.0001. In order to make the model convergence faster and better, we use Resnet-50, which is pre-trained on Imagenet. In the process of training, we use the data enhancement strategy such as random flipping. In order to compare all methods fairly, we also use the same data enhancement strategy and pre-trained model for other methods. All other hyper-parameters follow the settings in mmdetection [60]. 4.3 Main results We compare the proposed method with the state-of-the-art detection methods: Faster R-CNN [40], Cascade R-CNN [41], FreeAnchor [52], FoveaBox [51] and RetinaNet [48], mainly by comparing the AP and sensitivity. As shown in Fig. 6, the AP value of our method is higher than that of other methods. We also compare the sensitivity at different false positive rates and find that the results of our methods are better than those of these methods, as shown in Fig. 7. Fig. 6Open in figure viewerPowerPoint AP of different methods Fig. 7Open in figure viewerPowerPoint Sensitivity at different false positive rates of different methods Figs. 8-11 are the comparisons between our method and RetinaNet. From Figs. 8 and 9, we can see that: our method has more advantages in extracting details and can better perceive the details, it can effectively avoid missing detection. From Figs. 10 and 11, we can see that: our method can better perceive the global information. Some cells may be similar to pathological cells from the local point of view, but they are not cancer cells from the global point of view, so it can effectively avoid the situation of false detection. Fig. 8Open in figure viewerPowerPoint First example: Comparison between our method and RetinaNet. Avoid missing detection Fig. 9Open in figure viewerPowerPoint Second example: Comparison between our method and RetinaNet. Avoid missing detection Fig. 10Open in figure viewerPowerPoint Third example: Comparison between our method and RetinaNet. Avoid the situation of false detection Fig. 11Open in figure viewerPowerPoint Fourth example: Comparison between our method and RetinaNet. Avoid the situation of false detection 4.4 Ablation experiments In order to understand the help of the RM module and the SM module on performance improvement, we conduct ablation experiments with RetinaNet as an example, and the experimental results (AP and Sensitivity at 4 false positives per image) are shown in Table 2. Table 2. Ablation experiments of RM and SM Method AP (%) Sensitivity at 4 FPs RetinaNet 58.1 0.84 RetinaNet+SM 64.3 (+6.2) 0.8652 (+0.0252) RetinaNet+RM+SM 65.3 (+7.2) 0.8679 (+0.0279) Bold values indicate the result of the proposed method. In order to verify the performance of the CCDB module, we add it to different object detection methods: Faster R-CNN [40], Cascade R-CNN [41], FreeAnchor [52], FoveaBox [51] and RetinaNet [48]. We find that after adding the CCDB module, the performance of these methods in cervical cancer detection task has improved in varying degrees. In order to make a fair comparison, these methods use FPN to extract features, and the backbone network is Resnet-50. It can be seen from Table 1 that our CCDB module will not introduce too many parameters. After adding the CCDB module, the parameters are basically the same, but the AP value has been greatly improved. From Table 3, we can see that the sensitivity at different false positive rates has been improved with the CCDB module. Table 3. Ablation Experiments of CCDB were evaluated by sensitivity at differentfalse positive rates. Method 0.5 1 2 4 8 16 Faster R-CNN [40] 0.5624 0.6747 0.7708 0.8373 0.8526 – Faster R-CNN+CCDB 0.6325 (+0.0701) 0.743 (+0.0683) 0.8131 (+0.0423) 0.8679 (+0.0306) 0.8724 (+0.0198) – Cascade R-CNN [41] 0.5624 0.6819 0.7771 0.8247 – – Cascade R-CNN+CCDB 0.6208 (+0.0584) 0.735 (+0.0539) 0.8122 (+0.0351) 0.8517 (+0.027) – – FreeAnchor [52] 0.5166 0.6325 0.7439 0.8292 0.8885 0.9415 FreeAnchor+CCDB 0.5884 (+0.0718) 0.6819 (+0.0494) 0.7637 (+0.0198) 0.8436 (+0.0144) 0.9083 (+0.0198) 0.9487 (+0.0072) FoveaBox [51] 0.5435 0.6612 0.7565 0.8481 0.8957 0.9353 FoveaBox+CCDB 0.6019 (+0.0584) 0.7196 (+0.0584) 0.8059 (+0.0494) 0.8607 (+0.0126) 0.9065 (+0.0108) 0.9389 (+0.0036) RetinaNet [48] 0.5417 0.6693 0.7628 0.84 0.9065 0.9442 RetinaNet+CCDB 0.6226 (+0.0809) 0.7151 (+0.0458) 0.8005 (+0.0377) 0.8679 (+0.0279) 0.911 (+0.0045) 0.9514 (+0.0072) Bold indicates the sensitivity value after adding the CCDB module. 4.5 Comparison evaluation with state-of-the-art cervical cancer detection methods We compare the proposed method with other cervical cancer detection methods. In order to provide a more comprehensive introduction, we also introduce the classification methods of cervical cancer [32, 68-72]. The experimental results are shown in Table 4. The commonly used dataset for the classification of cervical cancer is Herlev [28], and the evaluation metrics of the classification results are A