Cervical Smear Images Research Articles

New insight in cervical cancer diagnosis using convolution neural network architecture

<p>The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), root mean square propagation (RMSprop), Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis.</p>

Cervical cell's nucleus segmentation through an improved UNet architecture.

Precise segmentation of the nucleus is vital for computer-aided diagnosis (CAD) in cervical cytology. Automated delineation of the cervical nucleus has notorious challenges due to clumped cells, color variation, noise, and fuzzy boundaries. Due to its standout performance in medical image analysis, deep learning has gained attention from other techniques. We have proposed a deep learning model, namely C-UNet (Cervical-UNet), to segment cervical nuclei from overlapped, fuzzy, and blurred cervical cell smear images. Cross-scale features integration based on a bi-directional feature pyramid network (BiFPN) and wide context unit are used in the encoder of classic UNet architecture to learn spatial and local features. The decoder of the improved network has two inter-connected decoders that mutually optimize and integrate these features to produce segmentation masks. Each component of the proposed C-UNet is extensively evaluated to judge its effectiveness on a complex cervical cell dataset. Different data augmentation techniques were employed to enhance the proposed model's training. Experimental results have shown that the proposed model outperformed extant models, i.e., CGAN (Conditional Generative Adversarial Network), DeepLabv3, Mask-RCNN (Region-Based Convolutional Neural Network), and FCN (Fully Connected Network), on the employed dataset used in this study and ISBI-2014 (International Symposium on Biomedical Imaging 2014), ISBI-2015 datasets. The C-UNet achieved an object-level accuracy of 93%, pixel-level accuracy of 92.56%, object-level recall of 95.32%, pixel-level recall of 92.27%, Dice coefficient of 93.12%, and F1-score of 94.96% on complex cervical images dataset.

Data Discernment for Affordable Training in Medical Image Segmentation.

Collecting sufficient high-quality training data for deep neural networks is often expensive or even unaffordable in medical image segmentation tasks. We thus propose to train the network by using external data that can be collected in a cheaper way, e.g., crowd-sourcing. We show that by data discernment, the network is able to mine valuable knowledge from external data, even though the data distribution is very different from that of the original (internal) data. We discern the external data by learning an importance weight for each of them, with the goal to enhance the contribution of informative external data to network updating, while suppressing the data that are 'useless' or even 'harmful'. An iterative algorithm that alternatively estimates the importance weight and updates the network is developed by formulating the data discernment as a constrained nonlinear programming problem. It estimates the importance weight according to the distribution discrepancy between the external data and the internal dataset, and imposes a constraint to drive the network to learn more effectively, compared with the network without using the external data. We evaluate the proposed algorithm on two tasks: abdominal CT image and cervical smear image segmentation, using totally 6 publicly available datasets. The effectiveness of the algorithm is demonstrated by extensive experiments. Source codes are available at: https://github.com/YouyiSong/Data-Discernment.

Cervical cell classification with deep-learning algorithms.

Cervical cancer is a serious threat to the lives and health of women. The accurate analysis of cervical cell smear images is an important diagnostic basis for cancer identification. However, pathological data are often complex and difficult to analyze accurately because pathology images contain a wide variety of cells. To improve the recognition accuracy of cervical cell smear images, we propose a novel deep-learning model based on the improved Faster R-CNN, shallow feature enhancement networks, and generative adversarial networks. First, we used a global average pooling layer to enhance the robustness of the data feature transformation. Second, we designed a shallow feature enhancement network to improve the localization and recognition of weak cells. Finally, we established a data augmentation network to improve the detection capability of the model. The experimental results demonstrate that our proposed methods are superior to CenterNet, YOLOv5, and Faster R-CNN algorithms in some aspects, such as shorter time consumption, higher recognition precision, and stronger adaptive ability. Its maximum accuracy is 99.81%, and the overall mean average precision is 89.4% for the SIPaKMeD and Herlev datasets. Our method provides a useful reference for cervical cell smear image analysis. The missed diagnosis rate and false diagnosis rate are relatively high for cervical cell smear images of different pathologies and stages. Therefore, our algorithms need to be further improved to achieve a better balance. We will use a hyperspectral microscope to obtain more spectral data of cervical cells and input them into deep-learning models for data processing and classification research. First, we sent training samples of cervical cells into our proposed deep-learning model. Then, we used the proposed model to train eight types of cervical cells. Finally, we utilized the trained classifier to test the untrained samples and obtained the classification results. Fig 1. Deep-learning cervical cell classification framework.

Cervical Cell Image Classification-Based Knowledge Distillation.

Current deep-learning-based cervical cell classification methods suffer from parameter redundancy and poor model generalization performance, which creates challenges for the intelligent classification of cervical cytology smear images. In this paper, we establish a method for such classification that combines transfer learning and knowledge distillation. This new method not only transfers common features between different source domain data, but also realizes model-to-model knowledge transfer using the unnormalized probability output between models as knowledge. A multi-exit classification network is then introduced as the student network, where a global context module is embedded in each exit branch. A self-distillation method is then proposed to fuse contextual information; deep classifiers in the student network guide shallow classifiers to learn, and multiple classifier outputs are fused using an average integration strategy to form a classifier with strong generalization performance. The experimental results show that the developed method achieves good results using the SIPaKMeD dataset. The accuracy, sensitivity, specificity, and F-measure of the five classifications are 98.52%, 98.53%, 98.68%, 98.59%, respectively. The effectiveness of the method is further verified on a natural image dataset.

VisionCervix: Papanicolaou cervical smears classification using novel CNN-Vision ensemble approach

About half a million women in the world are affected by cervical cancer and about 0.3 million deaths occur per year due to cervical cancer. Cytologists perform Pap-smear tests to screen the Pap Smear images of the cervical cells. This manual screening is prone also to error. Therefore, an automated computer-aided detection systems have been proposed for the classification of cervical cancer cell images. In the proposed work, an ensemble of Vision Transformer network (ViT) and convolution neural network (CNN) has been proposed for the classification of cervical cell Pap smear images. ViT has been known for its minimal inductive bias and its competitive classification performance in comparison to the state-of-the-art convolution neural network. Fine-tuning large ViT network is a computationally intensive procedure; therefore, as an alternative to ViT-CNN approach, another transfer learning-based approach has also been proposed in which the features extracted from the pre-trained CNNs are combined and classified with the resource-efficient Long Short Term Memory (LSTM) network. Comparison between both the approaches has been made on the basis of their classification performance, test time, generalization ability and attention maps. Experimental results show that the ViT-CNN ensemble approach achieved 97.65% classification accuracy whereas the LSTM-based approach achieved 95.80% classification accuracy. ViT-CNN ensemble approach achieves better classification accuracy at the cost of the huge demand for computation since it takes more computational resources in terms of high amount of random access memory (RAM) in the graphical processing unit (GPU); whereas, the CNN-LSTM approach is less accurate and computationally cheaper.

CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. However, manual inspection is very troublesome, and experts are prone to make mistakes. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model.

An Improved Cervical Cell Segmentation Method Based on Deep Convolutional Network

The cervical cytology smear test is an effective method for cervical cancer early screening, and segmentation accuracy is essential for computer-aided diagnosis. In this study, an improved cervical nucleus and cytoplasm segmentation method based on a deep convolutional network was proposed. This method consisted of a cellular region proposal and pixel-level segmentation network (CRP-PSN). Data were obtained from the 2014 International Symposium on Biomedical Imaging cervical cell segmentation competition open dataset. In CRP networks, online hard example mining and soft-nonmaximum suppression algorithms were incorporated to solve problems, including background noise, impurities, and other interferences in smear images. For PSN networks, a generative adversarial network-generated adversarial network algorithm and least-squares loss function were used to generate cell region segmentation, thereby improving the cytoplasm segmentation results. Finally, the improved CRP-PSN model was analyzed and compared with other typical cervical cell segmentation methods. The experimental results showed that the proposed model could effectively improve the segmentation accuracy of cytoplasm and nuclei in cervical cytology smear images by 92% and 98.6%, respectively. These findings provided strong support for the application of this method for automated interpretation of cervical cytology smear images and improved diagnostic reliability.

Autofocus method based on multi regions of interest window for cervical smear images

Autofocus method based on multi regions of interest window for cervical smear images

Extraction and Separation of Overlapped Squamous Cell Cytoplasm with Disjoint Nuclei in Cervical Pap Smear Image

The automated analysis of Papanicolaou (Pap) smear images is a challenging issue due to the occlusion of cells. The feature extraction and interpretation depend on the accuracy of cell segmentation. This is important to find the difference between normal and abnormal cells. However, most of the time complications are due to overlapping in the Pap smear cells. The overlapping can be in nuclei regions or cytoplasmic regions or sometimes both the regions. There are two types of cells found in Pap smear images – Columnar and Squamous cells. Most of the time abnormalities are found in squamous cells which in turn may lead to cervical cancer. Hence, this work concentrates on the separation of squamous cells in which cytoplasmic regions are overlapped with disjoint nuclei regions. The main objective of this work is to identify the intersecting points where cytoplasmic regions of two cells meet, called Concavity points, which are calculated using a scope of points along the cytoplasmic boundary of the cells, and separating the cells along with these concavity points.

Five-Class Classification of Cervical Pap Smear Images: A Study of CNN-Error-Correcting SVM Models.

ObjectivesDifferent complex strategies of fusing handcrafted descriptors and features from convolutional neural network (CNN) models have been studied, mainly for two-class Papanicolaou (Pap) smear image classification. This paper explores a simplified system using combined binary coding for a five-class version of this problem.MethodsThis system extracted features from transfer learning of AlexNet, VGG19, and ResNet50 networks before reducing this problem into multiple binary sub-problems using error-correcting coding. The learners were trained using the support vector machine (SVM) method. The outputs of these classifiers were combined and compared to the true class codes for the final prediction.ResultsDespite the superior performance of VGG19-SVM, with mean ± standard deviation accuracy and sensitivity of 80.68% ± 2.00% and 80.86% ± 0.45%, respectively, this model required a long training time. There were also false-negative cases using both the VGGNet-SVM and ResNet-SVM models. AlexNet-SVM was more efficient in terms of running speed and prediction consistency. Our findings also showed good diagnostic ability, with an area under the curve of approximately 0.95. Further investigation also showed good agreement between our research outcomes and that of the state-of-the-art methods, with specificity ranging from 93% to 100%.ConclusionsWe believe that the AlexNet-SVM model can be conveniently applied for clinical use. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well as an appropriate selection of experimental design to improve the efficiency of Pap smear image classification.

A contour property based approach to segment nuclei in cervical cytology images

BackgroundSegmentation of nuclei in cervical cytology pap smear images is a crucial stage in automated cervical cancer screening. The task itself is challenging due to the presence of cervical cells with spurious edges, overlapping cells, neutrophils, and artifacts.MethodsAfter the initial preprocessing steps of adaptive thresholding, in our approach, the image passes through a convolution filter to filter out some noise. Then, contours from the resultant image are filtered by their distinctive contour properties followed by a nucleus size recovery procedure based on contour average intensity value.ResultsWe evaluate our method on a public (benchmark) dataset collected from ISBI and also a private real dataset. The results show that our algorithm outperforms other state-of-the-art methods in nucleus segmentation on the ISBI dataset with a precision of 0.978 and recall of 0.933. A promising precision of 0.770 and a formidable recall of 0.886 on the private real dataset indicate that our algorithm can effectively detect and segment nuclei on real cervical cytology images. Tuning various parameters, the precision could be increased to as high as 0.949 with an acceptable decrease of recall to 0.759. Our method also managed an Aggregated Jaccard Index of 0.681 outperforming other state-of-the-art methods on the real dataset.ConclusionWe have proposed a contour property-based approach for segmentation of nuclei. Our algorithm has several tunable parameters and is flexible enough to adapt to real practical scenarios and requirements.

Improvement method for cervical cancer detection: A comparative analysis.

Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the method selected. There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab, and the dataset used is established Herlev Dataset. The results show that the highest performance assessment metric values obtain from Method 1: Thresholding and Trace region boundaries in a binary image with the values of precision 1.0, sensitivity 98.77%, specificity 98.76%, accuracy 98.77% and PSNR 25.74% for a single type of cell. Meanwhile, the average values of precision were 0.99, sensitivity 90.71%, specificity 96.55%, accuracy 92.91% and PSNR 16.22%. The experimental results are then compared to the existing methods from previous studies. They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values. On the other hand, the majority of current approaches can be used with either a single or a large number of cervical cancer smear images. This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.

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

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.

Statistical Analysis of Cervical Overlapping Cells and Identification of Lesion Cells Based on Sparse Morphological Learning Model

Clinically, the microscopic images of cervical cell smears are complex and changeable. Besides, the individual’s fluctuation is large, and the environment of dyeing is fluctuant, which causes the cervical cells in the smear microscopic images are of a complex category, and a large number of cellular agglomerations and adhesions when hyperplasia is active. Therefore, we proposed a statistical analysis of cervical overlapping cells and identification of lesion cells based on morphological learning model, which adopts sparse representation and mathematical morphological operation to achieve the segmentation of the complex cell from clinical smear microscopic images. The segmentation results demonstrate that the proposed method has the best segmentation performance and excellent generalization capability. It can be applied in the clinical application of cervical cytology images analysis. The proposed method has certain theoretical significance and application value.

The Use of Static Telecytology for Quality Assessment Purposes in the Evaluation of Cervical Smears Prepared by Means of Liquid-Based Cytology.

Objective: The objective of this study was to investigate the role of telecytology as a tool with increased quality standards in the optimal evaluation of telecytological diagnoses proffered on the basis of digitized images from cervical smears prepared by means of liquid-based cytology. Materials and Methods: The study was performed on representative digital cytological images from a total of 808 cervical smears (benign, 270; atypical squamous cells of undetermined significance, 184; low-grade squamous intraepithelial lesion, 124; high-grade squamous intraepithelial lesion, 174; squamous cell carcinoma, 52; and adenocarcinoma, 4) that were transferred through file transfer protocol to password-protected accounts for remote review by three independent cytopathologists. In addition to diagnosis, reviewers also commented on overall digital image quality. Contributor's and reviewer's diagnoses were collected, recorded, and statistically evaluated. Their reports were recorded and classified. Results: Statistical evaluation of cytological diagnoses detected no significant difference in diagnostic accuracy between the diagnoses proffered on the basis of digitized images and conventional slides. The overall interobserver agreement was almost perfect with κ values of 0.79-0.97. Conclusions: Static telecytology can be used as an alternative method for the cytological diagnosis of cervical smears, particularly in quality assurance programs. It is a prompt and valid method for quality assessment and proficiency testing and can be integrated into daily workflow. Digital images of cervical smears can be used for rapid and accurate diagnosis, by diminishing turnaround times and improving small cytology departments' quality indices.

Adaptive Pruning of Transfer Learned Deep Convolutional Neural Network for Classification of Cervical Pap Smear Images

Automatic classification of cervical Pap smear images plays a key role in computer-aided cervical cancer diagnosis. Conventional classification approaches rely on cell segmentation and feature extraction methods. Due to overlapping cells, dust, impurities and uneven irradiation, the accurate segmentation and feature extraction of Pap smear images are still challenging. To overcome the difficulties of the feature-based approaches, deep learning is becoming more important alternative. Since the number of cervical cytological images is limited, an adaptive pruning deep transfer learning model (PsiNet-TAP) is proposed for Pap smear images classification. We designed a novel network to classify Pap smear images. Due to the limited number of images, we adopted transfer learning to obtain the pre-trained model. Then it was optimized by modifying the convolution layer and pruning some convolution kernels that may interfere with the target classification task. The proposed method PsiNet-TAP was tested on 389 cervical Pap smear images. The method has achieved remarkable performance (accuracy: more than 98%), which demonstrates the strength of the proposed method for providing an efficient tool for cervical cancer classification in clinical settings.

Nucleus Segmentation of Cervical Cytology Images Based on Depth Information

In cervical cancer screening, accurate segmentation of cervical nucleus is a key part in the early diagnosis of cervical cancer. However, the cervical nucleus segmentation faces many great challenges owing to the overlapping cervical cells, uneven staining and poor contrast of cervical cytology smear images. In this paper, a tree domain structure and screening algorithm based on depth-first searching strategy are proposed to obtain candidate nucleus regions according to the annular clustering characteristics of nucleus depth information in cervical cytology images. Then, the candidate nucleus regions are finely segmented with an iterative level set algorithm based on adaptive radius morphological dilation. Experimental results are evaluated on the ISBI2015 public dataset. The performance of the proposed nucleus segmentation algorithm is higher than that of the state-of-the-art methods in terms of positive predictive value, negative predictive value, precision, recall of the cervical nucleus segmentation.

A Parametric Optimization Oriented, AFSA Based Random Forest Algorithm: Application to the Detection of Cervical Epithelial Cells

Cervical cancer is one of the most common cancers among women in the world, over 570,000 patients are affected annually. Pathological examination for patients using Pap Smear becomes the mainstream of cervical cancer diagnoses. Accurate diagnoses and analyses largely rely on 3 factors: cell segmentation, feature extraction and selection as well as classification. Firstly, a 2-layer segmentation algorithm based on block Maximum Between-Class Variance (Otsu) and Gradient Vector Flow (GVF) Snake model is applied to obtain regions of interest (ROI). Then the features of chroma, shape and texture are extracted and selected for a better classification performance. The random forest algorithm based on Artificial Fish Swarms Algorithm (AFSA) is used to recognize and classify cervical epithelial cells. The proposed methods were tested on 200 cervical Pap Smear images. Experimental results show that cervical cells can be segmented with an effective segmentation result. The proposed feature selection method achieved an accuracy of 81.31% with the minimum feature number. The improved random forest algorithm with 2 and 7 classification under fivefold cross-validation reaches the highest classification accuracy (96.86%). Experimental results showed that the proposed method has obvious recognition advantages, and thus provides a practical classification frame for the diagnoses of cervical cancer.

Polar coordinate sampling-based segmentation of overlapping cervical cells using attention U-Net and random walk

Segmentation of nuclei and cytoplasm inside the cellular clumps in cervical smear images is a difficult task because of the poor contrast and unpredictable shape of cytoplasm. This article addresses a new framework based on Attention U-Net (ATT U-Net) network and graph-based Random Walk (RW) to extract both nucleus and cytoplasm of each individual cell within an image of overlapping cervical cells. The proposed approach starts by separating nuclei from the cellular clumps through ATT U-Net architecture. Then, we remove fake nuclei that are usually much smaller than real nuclei. For each nucleus, a polar coordinate sampling matrix is generated. Each element in this matrix is realized by converting the image pixel from Cartesian coordinates to polar coordinates. After that, converted images would serve as the input of ATT U-Net for predicting cytoplasm. And finally, Graph-based RW is applied to extract the contour of cytoplasm. Because the features of cytoplasm boundaries in predicted maps are so obvious that the segmentation of every individual cell, including overlapping area, worked well under RW. We evaluate our framework on ISBI 2014 Challenge Dataset. The results reveal that our approach improves the performance on extracting individual cell from heavy overlapping cell clumps.