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Postcoital bleeding is a worrying, for both women and health care provider. This study aimed to evaluate the prevalence of abnormal cervical cytology among patient with PCB in the private outpatient care setting, and to identify risk-factors for cervical pathology among these patients. A cross sectional study of 90 patients presented to the privet clinic, during the period 1st feb-1st Des. 2019. Inclusion criteria include married women presented with post coital bleeding, non-pregnant, all patients undergo Pap Smear Cytology. The commonest age presented with PCB was 40-44 years old 22(24.4%), followed by (45-49) years and (25-29) years 18(20%). Those aged ≥40 years 44(48.9%), and those aged ≤34 34(37.8%). The mean parity was 4.7±2.8, hormonal contraception was commonly used by those with PCB followed by intra uterine contraceptive device (IUCD), 30(33.3%), 18(20) respectively. Most of the patient were House wives 78(86.7%), The physical examination show that most patient had cervical erosion 68(75.5%), followed by healthy cervix in 12(13.3%), while suspicious features was found among 6(6.7%), Moderate and sever dysplasia was found more among those aged ≥40 years 10(22.7%), while it was 4(8.7%) among those aged < 40 years. there is higher prevalence of High-grade squamous intraepithelial lesions among females with PCB, and non- significant difference among those aged less or more than 40 years. Therefore it’s important to examine any patient complaining of PCB with PAP smear cytology and colposcopy examination.

Objectives: In the cervical smear screening test as a sample collection method for liquid-based thin layer cytology, classically the collecting device is placed into a liquid fixative solution and vigorously swirled or rotated ten times in the solution and the collection device is removed from the solution. In this study, a plastic smear brush was used as the collection device. After the cervical cell sample was obtained, the smear brush was detached from the stick and left in the solution and given to the laboratory. Our aim in the study is to examine whether smear inadequacy rates have decreased with the method used in the study compared to the classical method. Material and methods: While the classical technique which the collecting device is placed into a solution and mixed and removed from the solution is defined as Method 1. The technique used in the study was defined as Method 2. The cervical smear screening test results obtained by two different methods in two consecutive time periods were analyzed. The two methods were compared using chi-square test in terms of smear inadequacy. Results: A total of 2129 test results, including 1129 smears in Method 1 and 1000 smears in Method 2 were examined. The mean ages of the patients tested in both methods were similar (36 ± 6.1 and 37 ± 6.7). Abnormal test result rate was similar for Method 1 and Method 2 (5.8% vs 4.9%, respectively). The inadequate sample rate was higher in Method 1 than Method 2 (8.3% vs 2.1%, respectively). Conclusions: The study showed that leaving the smear brush in the solution is a better way to reduce the inadequacy sample rates. This result may guide clinicians about smear techniques.

Cervical cancer has influenced life of women worldwide as the fourth most severe cancer. Early screening, detection and treatment of cervical cancer notably increase the life quality and reduce the death rate of patients. Therefore, automatic diagnosis of cervical cancer could bridge the gap between testing needs and capabilities. Cervical cell detection plays an important role in cancer screening, the intent of this study is to classify the cervical cells through deep learning models, which helps to monitor the patients’ health. SSD (Single Shot MultiBox Detector) network is integrated with the positive and negative features to address the shortcomings of insufficient sensitivity to small objects. Besides, center loss function is added to better address situations that intra-class differences are greater than inter-class differences. A dataset containing 1462 benchmarked cervical cells was utilized. 80% (1167) are used for training and the remaining 20% (295) are allocated for testing. Proposed optimized SSD network achieved the accuracy of 90.8% and mAP (mean Average Precision) of 81.53%, which is 7.54% and 4.92% higher than YOLO (You Only Look Once) and classical SSD, respectively. The addition of complementary features improves the network sensitivity and the overall accuracy. It is also concluded that the proposed SSD network could be applied in cell classification for the early automatic detection of cervical cancer.

Background: Early detection of cervical cancer may give life to women all over the world. Pap-smear test and Human papillomavirus test are techniques used for the detection and prevention of cervical cancer. Objective: In this paper, pap-smear images are analysed and cells are classified using stacked autoencoder based deep neural network. Pap-smear cells are classified into 2 classes and 4 classes. Twoclass classification includes classification of cells in normal and abnormal cells while four-class classification includes classification of cells in normal cells , mild dysplastic cells, moderate dysplastic cells and severe dysplastic cells. Methods: The features are extracted by deep neural networks based on their architecture. Proposed deep neural networks consist of three stacked auto encoders with hidden sizes 512, 256 and 128, respectively. Softmax used as the outer layer for the classification of pap smear cells. Results: Average accuracy achieved for 2-class classification among normal and abnormal cells is 98.2 % while for 4-class classification among normal, mild, moderate and severe dysplastic cells is 93.8 % respectively. Conclusion: The proposed approach avoids image segmentation and feature extraction applied by previous works. This study highlights deep learning as an important tool for cells classification of pap-smear images. The accuracy of the proposed method may vary with the different combination of hidden size and number of autoencoders.

BACKGROUND: Bhutan implemented a national program for human papillomavirus (HPV) vaccination in 2010 involving girls aged 12 to 18 years and achieving nearly 90% coverage.OBJECTIVE: To estimate HPV vaccine effectiveness in a city in Bhutan.DESIGN: 2 cross-sectional surveys, 2011-2012 and 2018.SETTING: 2 hospitals in Thimphu, capital of Bhutan.PARTICIPANTS: Sexually active women aged 17 to 29 years: 1445 participants from the baseline survey and 1595 from the repeated survey.INTERVENTION: National HPV vaccination program.MEASUREMENTS: HPV was assessed in cervical cell samples by using general primer GP5+/GP6+-mediated polymerase chain reaction. Human papillomavirus types were stratified as vaccine types (HPV6/11/16/18) and nonvaccine types. Age- and sexual behavior-adjusted overall, total, and indirect (herd immunity) vaccine effectiveness (VE) was computed as (1 - HPV prevalence ratio) for HPV among all women and among unvaccinated women.RESULTS: Between the 2 surveys, the prevalence of HPV vaccine types decreased from 8.3% to 1.4%, whereas the prevalence of nonvaccine types increased from 25.8% to 31.4%. The overall and indirect adjusted VE against vaccine-targeted HPV types was 88% (95% CI, 80% to 92%) and 78% (CI, 61% to 88%), respectively. Among women younger than 27 years, who were targeted by the vaccination program, the overall and indirect adjusted VE was 93% (CI, 87% to 97%) and 88% (CI, 69% to 95%), respectively. No impact on nonvaccine HPV types was detectable.LIMITATION: Hospital-based recruitment; self-reported vaccination status.CONCLUSION: In Bhutan, the prevalence of vaccine-targeted HPV types has decreased sharply, providing the first evidence of the effectiveness of a high-coverage national HPV vaccination program in a lower-middle-income country.PRIMARY FUNDING SOURCE: Bill & Melinda Gates Foundation.

Cervical cancer if detected early has an upward of 89% survival rate. The leading tool in identifying cervical cancer in its infancy is the Papanicolaou (Pap smear) test, which since its introduction dropped cervical cancer related deaths by 60%. The Pap smear test or Liquid-based Cytology (LBC) is a time-consuming procedure that requires a pathologist to manually identify cervical cells that may be in the middle of the processes that indicate cervical cancer. Unfortunately, due to the expenses related to conducting the Pap smear test many women are blocked from access to it and this leads to over 4000 women dying annually from cervical cancer in the United States alone. The aim of this research is to automate the methods used by pathologists in conducting the Pap smear or LBC. We show that using machine vision, ensemble learning and deep learning methods a significant portion of the Pap smear can be done automated. We set out to extract cells and cell clusters and classify those samples based on the Bethesda System for reporting cervical cytology. Achieving an accuracy of 90.4% and 91.6% for the ensemble learning and deep learning methods respectively when evaluated with a five-fold cross-validation demonstrates promise in the creation of an automated Pap smear screening test.

In the screening of cervical cancer cells, accurate identification and segmentation of nucleus in cell images is a key part in the early diagnosis of cervical cancer. Overlapping, uneven staining, poor contrast, and other reasons present challenges to cervical nucleus segmentation. We propose a segmentation method for cervical nuclei based on a multi-scale fuzzy clustering algorithm, which segments cervical cell clump images at different scales. We adopt a novel interesting degree based on area prior to measure the interesting degree of the node. The application of these two methods not only solves the problem of selecting the categories number of the clustering algorithm but also greatly improves the nucleus recognition performance. The method is evaluated by the IBSI2014 and IBSI2015 public datasets. Experiments show that the proposed algorithm has greater advantages than the state-of-the-art cervical nucleus segmentation algorithms and accomplishes high accuracy nucleus segmentation results., Graphical Abstract

Purpose: A study is presented of the irradiation of cancerous cervical cell line HeLa loaded with a platinum salt, betamethasone and deoxyglucose. The presence of the platinum increases the free-ra...

Analysis of pap smear images under a microscope by experts is a laborious and time consuming task. Computer-assisted diagnostic (CAD) systems can simplify this tedious process and allow experts to focus on more critical cases. Effective screening and early diagnosis can help to detect precancerous cells and allow early treatment. In this study, we have used a deep learning approach for classification of cervical cell images which obtained from pap smear slides. The proposed method automatically classifies cervical cell images into five categories without using any pre-processing on raw input images. We have obtained the promising results as compared to the previous studies in the literature. The proposed model can give a second opinion to clinicians in their daily routines and help them to focus on more complex cases.

Cervical cancer is regarded as one of the most common type of cancer suffered by women in the world. However, it can be treated if being detected early. The main method of diagnosing this type of cancer is through Papanicolaou test, in which cervical cells are collected and examined under a microscope. The process of examining the cervical cells is often done manually and is labourious. Several methods have been proposed to automate this process using machine learning. This study explores three Convolutional Neural Network models (i.e., AlexNet, Inception-V3 and ResNet50-V2) in classifying cervical cells. Transfer learning was implemented with different sets of varying number of images. Herlev dataset was used in this study. In the Herlev dataset, images were classified into seven classes (i.e. four abnormal and three normal). The classification done was based on seven-class classification. Performance of each model was measured based on accuracy, precision and recall. Based on the result obtained, ResNet50-V2 performed the best with the accuracy, precision and recall compared to the other two models.

Pap smear is a microscopic screening process of cervical smear used to detect pre-cancerous and cancerous cells in the cervix region. Cell nuclei segmentation is one of the most significant task in cervical cell image analysis which aids in identifyi

Objective: To estimate the prevalence and to determine factor associated with abnormal cervical cell among the Hmong and Mien hill tribe women. Methods: A cross-sectional study aimed to estimate the prevalence and to determine the factors associated with abnormal cervical cell among the Hmong and Mien hill tribe women living in Pha Yao province, Thailand was conducted. The data from validated questionnaires and Papanicolaou (Pap) smear test results were collected. Logistic regression was used to detect the associations between variables and abnormal cervical cell at the significant level of alpha = 0.05. Results: Totally, 450 Hmong and Mien women were recruited into the analysis. The overall prevalence of abnormal cervical cell was 2.2%; 1.2% were atypical squamous cells of undetermined significance (ASC-US), 0.4% were atypical squamous cells-cannot exclude HSIL (ASC-H), 0.4% were cervical intraepithelium neoplasia-I (CIN-I) and 0.2% were cervical intraepithelium neoplasia-II (CIN-II). Two variables, the number of their sexual partners and the number of husbands’ sexual partners, were significantly associated with abnormal cervical cell. The women who had ≥ 4 sexual partners had a 7.09 times (95%CI=1.85-27.17) more likely to have abnormal cervical cell than those who had < 4 sexual partners. The women whose husbands had ≥ 4 partners had a 5.63 times (95%CI=1.51-20.90) more likely to have abnormal cervical cell than those whose husbands had < 4 sexual partners. Conclusion: The number of sexual partners is significantly associated with abnormal cervical cell among the Hmong and Mien hill tribe women. Health interventions regarding safe sex should be promoted in the hill tribe people.

In recent years, with the increasing incidence of cervical cancer, it is a tedious and time-consuming task with unsatisfying accuracy to manually recognize the cells. Machine recognition can be a good solution, but it suffers from the difficulty of obtaining precise edges of cells, which directly influence the final recognition accuracy. To improve the recognition accuracy and shorten the time used for cell recognition, an AGVF-Snake (Adaptive Gradient Vector Flow-Snake) model for the extraction of cell edges has been proposed in this paper. Firstly, the cell is initially located by the improved Canny algorithm. Then, the adaptive initial contour model and gradient vector model are used to obtain accurate cell edges. Finally, the PSO–SVM (Particle Swarm Optimization-Support Vector Machine) classifier is selected to recognize the cervical cells. Herlev dataset is used to verify the AGVF-Snake algorithm; the accuracy of two and seven classifications are recorded. Six other classification methods are introduced for comparison. According to the experimental results, the accuracy of two and seven classifications can achieve up to 99%, which are better than other six methods. The experiment results show that the proposed algorithm has obvious recognition advantages, and thus provides an effective methodological framework for the diagnosis of cervical cancer diseases.

Determining the spatial distribution of human papillomavirus (HPV) and performing accurate public health analyses helps to distinguish areas of healthcare that require further research, and enables therapeutic techniques and approaches in healthcare to be focused more accurately. A total of 4,560 women were enrolled in the present study. Flow-through hybridization and gene chip assays were used to detect the genotypes of HPV infection. Heat maps were then generated to present the spatial distribution of HPV infections in Zhejiang Province according to genotype. Of the exfoliated cervical cell samples from the 4,560 women, HPV was detected in 1,886 samples. HPV-16, -58, -52 and -18 were the most prevalently identified genotypes in the population included in the present study. HPV-16 and -58 infections were mainly distributed in the northern and central regions of Zhejiang Province, such as in Hangzhou and Shaoxing, where the prevalence was higher than that in the southern regions (P

Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early stages and treat them. Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors. To improve the manual screening practice, machine learning (ML) and deep learning (DL) based computer-aided diagnostic (CAD) systems have been investigated widely to classify cervical pap cells. Most of the existing researches require pre-segmented images to obtain good classification results, whereas accurate cervical cell segmentation is challenging because of cell clustering. Some studies rely on handcrafted features, which cannot guarantee the classification stage's optimality. Moreover, DL provides poor performance for a multiclass classification task when there is an uneven distribution of data, which is prevalent in the cervical cell dataset. This investigation has addressed those limitations by proposing DeepCervix, a hybrid deep feature fusion (HDFF) technique based on DL to classify the cervical cells accurately. Our proposed method uses various DL models to capture more potential information to enhance classification performance. Our proposed HDFF method is tested on the publicly available SIPAKMED dataset and compared the performance with base DL models and the LF method. For the SIPAKMED dataset, we have obtained the state-of-the-art classification accuracy of 99.85%, 99.38%, and 99.14% for 2-class, 3-class, and 5-class classification. Moreover, our method is tested on the Herlev dataset and achieves an accuracy of 98.32% for binary class and 90.32% for 7-class classification., 12 pages, 8 figures, Published in Computers in Biology and Medicine

To apply topological information to solve a cervical histopathology image clustering (CHIC) problem, a graph based unsupervised learning (GBUL) approach is proposed in this paper. First, the GBUL method applies color features and k-means clustering to carry out a first-stage “coarse” clustering. Then, a skeletonization based node generation (SBNG) approach is introduced to approximate the distribution of cervical cell nuclei. Thirdly, based on the SBNG nodes, multiple graphs are constructed. Next, graph features are extracted based on the constructed graphs. Finally, k-means clustering is used again for the second-stage clustering. In the experiment, a practical Hematoxylin–eosin staining cervical histopathology image dataset with 40 whole-slide imaging images is tested, obtaining a promising CHIC result and showing a huge potential in the cancer risk prediction field.

Cervical cancer is one of the primary factors that endanger women’s health, and Thinprep cytologic test (TCT) is the common testing tool for the early diagnosis of cervical cancer. However, it is tedious and time-consuming for pathologists to assess and find abnormal cells in many TCT samples. Thus, automatic detection of abnormal cervical cells is highly demanded. Nevertheless, false positive cells are inevitable after automatic detection. It is still a burden for the pathologist if the false positive rate is high. To this end, here we propose a semi-supervised cervical cell diagnosis method that can significantly reduce the false positive rate. First, we incorporate a detection network to localize the suspicious abnormal cervical cells. Then, we design a semi-supervised classification network to identify whether the cervical cells are truly abnormal or not. To boost the performance of the semi-supervised classification network, and make full use of the localizing information derived from the detection network, we use the predicted bounding boxes of the detection network as an additional constraint for the attention masks from the classification network. Besides, we also develop a novel consistency constraint between the teacher and student models to guarantee the robustness of the network. Our experimental results show that our network can achieve satisfactory classification accuracy using only a limited number of labeled cells, and also greatly reduce the false positive rate in cervical cell detection.

OBJECTIVE This study aims to discover the most prevalent high-risk (hr) HPV genotypes in the regions of Loreto and La Libertad, Peru. METHODS In 2015, cervical cell samples were collected during pelvic examinations from women in the geographically distinct regions of Loreto and La Libertad, Peru. In 2017, additional samples were collected in La Libertad. A total of 429 women between the ages of 18 and 65 years living in these regions were enrolled in the study. All samples were analyzed by polymerase chain reaction for the hrHPV genotypes 16, 18, and 35. RESULTS Sample collection from 126 women in 2015 in Loreto revealed an hrHPV incidence of 15.9% (20 of 126), with 60% (12 of 126) of HPV infections due to hrHPV 16. Samples from La Libertad revealed an hrHPV incidence of 14.5% incidence (44 of 303) (among 303 women). Of these infections, 38% (17) were attributable to hrHPV type 35 and 20% (9) were due to hrHPV type 16. CONCLUSION The prevalence of hrHPV genotypes in Peru may differ from those observed in North America and Europe. Loreto appears to follow the prevalence trend observed in North America, with hrHPV type 16 accounting for the majority of cases. However, hrHPV type 35 may account for a greater contribution to the cervical cancer burden in La Libertad. Further research, specifically on cervical tumor specimens, is needed.

A novel filtering algorithm is proposed based on level set method (LSM) and linear time Euclidean distance transform (LET) algorithm in this paper, which has the property of shape retention and thus is suitable for post-processing of the initial contours for contacting instances in digital Pap image. As one of our contributions, we propose two new metrics based on the pixel-level average false positive rate and false negative rate that used by baseline method. A significant decrease in pixel-level average false positive rate (FP) by 62% can obtain by our proposed method. The result of quantitative and qualitative evaluation shows that our proposed shape retentive filtering algorithm (SRFA) can effectively filter out the false positive fragments of the initial instance contour of cervical cells from the ISBI-2014 dataset.

Introduction: Alternative methods of dry storage and transportation may be a viable alternative to the use of liquid storage medium for cervical samples, especially for screening programs in places with few resources. Objective: The objective of this study is to verify the viability and efficacy of human papillomavirus DNA (HPV-DNA) detection in cervical cell samples collected and stored on a Flinders Technology Associates (FTA) card (Whatman Indicating FTA® Elute Micro Card) and subsequently recovered in ethanol-based liquid medium and to compare the results to those obtained using samples stored directly in ethanol-based liquid medium. Study Design: Thirty-four women submitted to ETZ (excision of the transformation zone of the cervix) were included in this study. Before ETZ, 2 samples of exfoliated cervical cells were collected from each woman by a doctor and stored in ethanol-based liquid medium and on an FTA card. DNA recovery from FTA samples was performed using ethanol-based liquid medium. Detection of HPV-DNA in the samples was performed using the Cobas® 4800 HPV Test Platform. Results and Conclusions: The HPV-DNA detection positivity rates were 70.6% for the samples collected directly in liquid medium and 64.7% for the samples stored on the FTA card, with high detection accuracy in the DNA samples recovered from the FTA card (area under the curve = 0.958; 95% confidence interval = 0.890–1.000). The concordance between the results obtained using the 2 storage media was 94.1% (Kappa = 0.866). These preliminary results suggest that collection of cervical material on an FTA card may be an alternative to storage in liquid medium since the liquid medium has some limitations. In addition, DNA recovery from the card using ethanol-based liquid medium streamlines the workflow in the laboratory and reduces the cost associated with reagents, thereby facilitating access to the HPV test in places with few resources and potentially improving cervical cancer screening.

Cervical smear screening is an imaging-based cancer detection tool, which is of pivotal importance for the early-stage diagnosis. A computer-aided screening system can automatically find out if the scanned whole-slide images (WSI) with cervical cells are classified as “abnormal” or “normal”, and then alert pathologists. It can significantly reduce the workload for human experts, and is therefore highly demanded in clinical practice. Most of the screening methods are based on automatic cervical cell detection and classification, but the accuracy is generally limited due to the high variation of cell appearance and lacking context information from the surroundings. Here we propose a novel and hierarchical framework for automatic cervical smear screening aiming at the robust performance of case-level diagnosis and finding suspected “abnormal” cells. Our framework consists of three stages. We commence by extracting a large number of pathology images from the scanned WSIs, and implementing abnormal cell detection to each pathology image. Then, we feed the detected “abnormal” cells with corresponding confidence into our novel classification model for a comprehensive analysis of the extracted pathology images. Finally, we summarize the classification outputs of all extracted images, and determine the overall screening result for the target case. Experiments show that our three-stage hierarchical method can effectively suppress the errors from cell-level detection, and provide an effective and robust way for cervical abnormality screening.