Pap Smear Slides Research Articles

Deep Learning-based Ensemble Approach for Conventional Pap Smear Image Classification

Cervical cancer screening allows the early signs of precancerous abnormalities in the cervix before they develop into invasive cancer. The Pap Smear is a widely used screening for early detection and prevention of cervical cancer. In many remote areas, the number of cytologists available to interpret pap smear screening tests is insufficient. This lack of personnel makes the test interpretation very time-consuming. To address this, deep learning techniques have been employed to detect cervical cancer cells and support cytologists. Therefore, an integrative approach with deep learning models and the ensemble techniques such as the maximum occurrence and the maximum probability score of cervical cells was proposed. The multi-cell assessment of the Pap smear slide allowed aggregate predictions of single cervical cell images using the proposed method. The classification results between pre-trained deep learning models and the proposed method were compared. In the experimental results, the proposed method can achieve an accuracy score of more than 97%, while the best pre-trained deep learning model can attain an accuracy score of more than 85%. Hence, the proposed method may have the potential to assist physicians or cytologists in the classification of cervical cell types for Pap Smear images.

Predictive Value of Various Atypical Cells for the Detection of Human Papillomavirus in Cervical Smears.

It is thought that numerous genotypes of human papillomavirus (HPV) are associated with various atypical cells, such as multinucleated cells, koilocytes, binucleated cells, parakeratotic cells, and giant cells, in the cervix. We previously showed the specificity of HPV genotypes for koilocytes and multinucleated cells. Therefore, in this study, we analyzed the association among HPV genotypes and binucleated cells, parakeratotic cells, and giant cells in Papanicolaou (Pap) smears. We detected HPV genotypes and atypical cells in 651 cases of liquid-based cytology with an abnormal Pap smear. The HPV genotypes associated with atypical cells were evaluated using stepwise logistic regression with backward elimination and a likelihood ratio test for model construction. Polymerase chain reaction was used to determine the HPV genotypes in whole liquid-based cytology samples and microdissected cell samples from Pap smear slides. Binucleated cells were significantly associated with HPV genotype 42. Moreover, parakeratotic cells were significantly associated with certain HPV genotypes, such as HPV40. However, it was difficult to detect specific HPV genotypes by the manual microdissection-polymerase chain reaction method despite the presence of binucleated cells and parakeratotic cells. Thus, the presence of binucleated cells, parakeratotic cells, and giant cells in Pap smears may not be predictive of cervical lesions above low-grade squamous intraepithelial lesions or infection with highly carcinogenic HPV genotypes.

CerCan·Net: Cervical cancer classification model via multi-layer feature ensembles of lightweight CNNs and transfer learning

Cervical cancer ranks among the most prevalent causes of fatality in women around the world. Early diagnosis is essential for treating cervical cancer using pap smear slides, but it is complex and time-consuming. Computer-aided diagnostic (CAD) systems based on deep learning models, particularly convolutional neural networks (CNNs), can facilitate the accurate and rapid diagnosis of cervical cancer. Recent CAD structures relied on using attributes extracted from a single layer of an individual CNN. However, distinct layers of CNNs are capable of specifying various-level knowledge, so merging deep features from various layers of a CNN could boost performance. Furthermore, incorporating deep features from multiple CNNs with various configurations could improve performance. This study proposes an efficient CAD entitled “CerCan·Net” for the automatic diagnosis of cervical cancer. CerCan·Net employs three lightweight CNNs with fewer parameters and lesser deep layers than that used in the literature, such as MobileNet, DarkNet-19, and ResNet-18, to reduce the complexity of classification. CerCan·Net engages transfer learning to obtain deep features out of the last three deep layers of each CNN, rather than using deep features of a specific layer. It also combines the features acquired by utilising the multi-CNN layers. Then, by maintaining a feature selection approach, it investigates the impact of generating a smaller set of deep features that could differentiate distinct subgroups of cervical cancer. CerCanNet performance is validated using two benchmark datasets, namely, the SIPaKMeD dataset and the Mendeley dataset. The results show that CerCan·Net is capable of achieving a high accuracy of 97.7% and 100% for the SIPaKMeD and Mendeley datasets using 400 and 200 features. Moreover, when comparing the performance of CerCan·Net with recent CADs, the comparison demonstrated its superiority. Therefore, it can be employed to assist cytopathologists in the automatic inspection, thereby avoiding limitations of routine diagnosis.

A Robust Deep Learning Approach for Accurate Segmentation of Cytoplasm and Nucleus in Noisy Pap Smear Images

Cervical cancer poses a significant global health burden, affecting women worldwide. Timely and accurate detection is crucial for effective treatment and improved patient outcomes. The Pap smear test has long been a standard cytology screening method, enabling early cancer diagnosis. However, to enhance quantitative analysis and refine diagnostic capabilities, precise segmentation of the cervical cytoplasm and nucleus using deep learning techniques holds immense promise. This research focuses on addressing the primary challenge of achieving accurate segmentation in the presence of noisy data commonly encountered in Pap smear images. Poisson noise, a prevalent type of noise, corrupts these images, impairing the precise delineation of the cytoplasm and nucleus. Consequently, segmentation boundaries become indistinct, leading to compromised overall accuracy. To overcome these limitations, the utilization of U-Net, a deep learning architecture specifically designed for automatic segmentation, has been proposed. This approach aims to mitigate the adverse effects of Poisson noise on the digitized Pap smear slides. The evaluation of the proposed methodology involved a dataset of 110 Pap smear slides. The experimental results demonstrate that the proposed approach successfully achieves precise segmentation of the nucleus and cytoplasm in noise-free images. By preserving the boundaries of both cellular components, the method facilitates accurate feature extraction, thus contributing to improved diagnostic capabilities. Comparative analysis between noisy and noise-free images reveals the superiority of the presented approach in terms of segmentation accuracy, as measured by various metrics, including the Dice coefficient, specificity, sensitivity, and intersection over union (IoU). The findings of this study underline the potential of deep-learning-based segmentation techniques to enhance cervical cancer diagnosis and pave the way for improved quantitative analysis in this critical field of women’s health.

Cervical Cancer Diagnosis Based on Multi-Domain Features Using Deep Learning Enhanced by Handcrafted Descriptors

Cervical cancer, among the most frequent adverse cancers in women, could be avoided through routine checks. The Pap smear check is a widespread screening methodology for the timely identification of cervical cancer, but it is susceptible to human mistakes. Artificial Intelligence-reliant computer-aided diagnostic (CAD) methods have been extensively explored to identify cervical cancer in order to enhance the conventional testing procedure. In order to attain remarkable classification results, most current CAD systems require pre-segmentation steps for the extraction of cervical cells from a pap smear slide, which is a complicated task. Furthermore, some CAD models use only hand-crafted feature extraction methods which cannot guarantee the sufficiency of classification phases. In addition, if there are few data samples, such as in cervical cell datasets, the use of deep learning (DL) alone is not the perfect choice. In addition, most existing CAD systems obtain attributes from one domain, but the integration of features from multiple domains usually increases performance. Hence, this article presents a CAD model based on extracting features from multiple domains not only one domain. It does not require a pre-segmentation process thus it is less complex than existing methods. It employs three compact DL models to obtain high-level spatial deep features rather than utilizing an individual DL model with large number of parameters and layers as used in current CADs. Moreover, it retrieves several statistical and textural descriptors from multiple domains including spatial and time–frequency domains instead of employing features from a single domain to demonstrate a clearer representation of cervical cancer features, which is not the case in most existing CADs. It examines the influence of each set of handcrafted attributes on diagnostic accuracy independently and hybrid. It then examines the consequences of combining each DL feature set obtained from each CNN with the combined handcrafted features. Finally, it uses principal component analysis to merge the entire DL features with the combined handcrafted features to investigate the effect of merging numerous DL features with various handcrafted features on classification results. With only 35 principal components, the accuracy achieved by the quatric SVM of the proposed CAD reached 100%. The performance of the described CAD proves that combining several DL features with numerous handcrafted descriptors from multiple domains is able to boost diagnostic accuracy. Additionally, the comparative performance analysis, along with other present studies, shows the competing capacity of the proposed CAD.

Privacy Preserved Cervical Cancer Detection Using Convolutional Neural Networks Applied to Pap Smear Images.

Image processing has enabled faster and more accurate image classification. It has been of great benefit to the health industry. Manually examining medical images like MRI and X-rays can be very time-consuming, more prone to human error, and way more costly. One such examination is the Pap smear exam, where the cervical cells are examined in laboratory settings to distinguish healthy cervical cells from abnormal cells, thus indicating early signs of cervical cancer. In this paper, we propose a convolutional neural network- (CNN-) based cervical cell classification using the publicly available SIPaKMeD dataset having five cell categories: superficial-intermediate, parabasal, koilocytotic, metaplastic, and dyskeratotic. CNN distinguishes between healthy cervical cells, cells with precancerous abnormalities, and benign cells. Pap smear images were segmented, and a deep CNN using four convolutional layers was applied to the augmented images of cervical cells obtained from Pap smear slides. A simple yet efficient CNN is proposed that yields an accuracy of 0.9113% and can be successfully used to classify cervical cells. A simple architecture that yields a reasonably good accuracy can increase the speed of diagnosis and decrease the response time, reducing the computation cost. Future researchers can build upon this model to improve the model's accuracy to get a faster and more accurate prediction.

HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides.

Cervical cancer is a critical imperilment to a female's health due to its malignancy and fatality rate. The disease can be thoroughly cured by locating and treating the infected tissues in the preliminary phase. The traditional practice for screening cervical cancer is the examination of cervix tissues using the Papanicolaou (Pap) test. Manual inspection of pap smears involves false-negative outcomes due to human error even in the presence of the infected sample. Automated computer vision diagnosis revamps this obstacle and plays a substantial role in screening abnormal tissues affected due to cervical cancer. Here, in this paper, we propose a hybrid deep feature concatenated network (HDFCN) following two-step data augmentation to detect cervical cancer for binary and multiclass classification on the Pap smear images. This network carries out the classification of malignant samples for whole slide images (WSI) of the openly accessible SIPaKMeD database by utilizing the concatenation of features extracted from the fine-tuning of the deep learning (DL) models, namely, VGG-16, ResNet-152, and DenseNet-169, pretrained on the ImageNet dataset. The performance outcomes of the proposed model are compared with the individual performances of the aforementioned DL networks using transfer learning (TL). Our proposed model achieved an accuracy of 97.45% and 99.29% for 5-class and 2-class classifications, respectively. Additionally, the experiment is performed to classify liquid-based cytology (LBC) WSI data containing pap smear images.

Nucleus segmentation and classification using residual SE-UNet and feature concatenation approach incervical cytopathology cell images.

Introduction: Pap smear is considered to be the primary examination for the diagnosis of cervical cancer. But the analysis of pap smear slides is a time-consuming task and tedious as it requires manual intervention. The diagnostic efficiency depends on the medical expertise of the pathologist, and human error often hinders the diagnosis. Automated segmentation and classification of cervical nuclei will help diagnose cervical cancer in earlier stages. Materials and Methods: The proposed methodology includes three models: a Residual-Squeeze-and-Excitation-module based segmentation model, a fusion-based feature extraction model, and a Multi-layer Perceptron classification model. In the fusion-based feature extraction model, three sets of deep features are extracted from these segmented nuclei using the pre-trained and fine-tuned VGG19, VGG-F, and CaffeNet models, and two hand-crafted descriptors, Bag-of-Features and Linear-Binary-Patterns, are extracted for each image. For this work, Herlev, SIPaKMeD, and ISBI2014 datasets are used for evaluation. The Herlev datasetis used for evaluating both segmentation and classification models. Whereas the SIPaKMeD and ISBI2014 are used for evaluating the classification model, and the segmentation model respectively. Results: The segmentation network enhanced the precision and ZSI by 2.04%, and 2.00% on the Herlev dataset, and the precision and recall by 0.68%, and 2.59% on the ISBI2014 dataset. The classification approach enhanced the accuracy, recall, and specificity by 0.59%, 0.47%, and 1.15% on the Herlev dataset, and by 0.02%, 0.15%, and 0.22% on the SIPaKMed dataset. Conclusion: The experiments demonstrate that the proposed work achieves promising performance on segmentation and classification in cervical cytopathology cell images..

Automatic whole slide pathology image diagnosis framework via unit stochastic selection and attention fusion

Pathology tissue slides are taken as the gold standard for the diagnosis of most cancer diseases. Automatic pathology slide diagnosis is still a challenging task for researchers because of the high-resolution, significant morphological variation, and ambiguity between malignant and benign regions in whole slide images (WSIs). In this study, we introduce a general framework to automatically diagnose different types of WSIs via unit stochastic selection and attention fusion. For example, a unit can denote a patch in a histopathology slide or a cell in a cytopathology slide. To be specific, we first train a unit-level convolutional neural network (CNN) to perform two tasks: constructing feature extractors for the units and for estimating a unit’s non-benign probability. Then we use our novel stochastic selection algorithm to choose a small subset of units that are most likely to be non-benign, referred to as the Units Of Interest (UOI), as determined by CNN. Next, we use the attention mechanism to fuse the representations of the UOI to form a fixed-length descriptor for the WSI’s diagnosis. We evaluate the proposed framework on three datasets: histological thyroid frozen sections, histological colonoscopy tissue slides, and cytological cervical pap smear slides. The framework achieves diagnosis accuracies higher than 0.8 and AUC values higher than 0.85 in all three applications. Experiments demonstrate the generality and effectiveness of the proposed framework and its potentiality for clinical applications.

The Association between Abnormal Vaginal Flora and Cytological Evidence of HPV with Prematurity in High-Risk Pregnant Women

Objective: To evaluate the association between abnormal vaginal flora and cytological evidence of HPV with prematurity in high-risk pregnant women. Study design: A prospective cohort study was designed with high-risk pregnant women who delivered singletons between 20-42 gestational weeks between January to November 2018. Vaginal specimens were collected to obtain material from the upper lateral vaginal vault and cervix for direct microscopic examination of vaginal contents and preparation of Gram-stained and pap smear slides. Potential determinants of infection were assessed using the chi-square test. Poisson regression was used to determine the prevalence ratio and 95% confidence interval of the association between the vaginal flora and cytology with prematurity and a p <0.05 was considered as statistical significance. Results: A total of 68 pap smear and high vaginal swab samples were collected from high-risk pregnant women with a mean age of 30.3 years. There were 26 (38.2%) cases of abnormal vaginal flora and 6 (8.8%) of HPV-induced cytological abnormalities. The preterm delivery rate was comparable between women with normal and abnormal vaginal flora (11.9% vs. 11.5%, prevalence ratio 1.0 [95% confidence interval: 0.8-1.2], aPR 1.0 [95% confidence interval: 0.8-1.2]) and between women with and without abnormal cytology (16.7% vs. 11.3%, prevalence ratio 0.9 [95% confidence interval: 0.6-1.4], aPR 0.9 [95% confidence interval: 0.6-1.4]). Conclusions: We determined no association between abnormal cytology or altered vaginal flora in high-risk pregnancy in terms of overall preterm birth rate.

Three segmentation techniques to predict the dysplasia in cervical cells in the presence of debris

Cervical Cancer is one of the most pandemic causes of cancer related death in females. Pap smear test is one of the most commonly used screening test for the cervical cancer. Existing algorithms focus on the segmentation of nucleus and cytoplasm either using single-cell images or multiple cells images. Images captured from the Pap smear slides are called smear images. Smear image contains cervical cells along with debris, debris are inflammatory cells, red blood cells, dye. Debris significantly influence the outcome of image segmentation. An accurate nuclei segmentation method can improve the success rate of cervical cancer screening. Therefore, this paper reveals about three segmentation techniques which are used for automated segmentation of cervical cell nuclei in the presence of debris. Three segmentation techniques namely, Automated Seed Region Growing, Extended Edge Based Detection and Modified Moving k-means techniques are proposed to extract the cervical cell nuclei. These techniques are extracting the area of nuclei from smear images using the morphological property of nucleus. Some debris have area that corresponds with the area of nucleus of normal cells, it may interfere with outcome and may give false positive results. The empirical area threshold value demonstrate the superior performance of all proposed methods. The qualitative and quantitative analysis also done on proposed techniques. Experimental analysis shows that Modified Moving k-means give favorable result in dysplasia detection in the presence of debris. A new dataset PapsmearJP is collected during the study with the help of a pathologist for the validation of the work.

Koilocytic changes are not elicited by human papillomavirus genotypes with higher oncogenic potential.

Koilocytes are considered a common cytopathological effect in patients with human papillomavirus (HPV) infection. Thus, we aimed to elucidate whether koilocytes are common to all HPV infections. Liquid-based cytology samples from 651 patients with abnormal Papanicolaou (Pap) test results were used to analyze the presence of koilocytes and HPV genotype. HPV genotype was determined in complete liquid cytology samples and microdissected cell samples from Pap smear slides using the uniplex E6/E7 polymerase chain reaction method, which can detect 39 mucosal HPV genotypes. Koilocytes were found in 29.3% (191) of all patients. Logistical regression analysis of diverse HPV genotypes revealed that infections with low-risk HPV types (HPV-6b, HPV-40, HPV-42, HPV-61, HPV-74, HPV-89, and HPV-90), probably high-risk HPV types (HPV-53 and HPV-66), and high-risk types (HPV-39 and HPV-56) were significantly associated with the presence of koilocytes. However, HPV-16, HPV-18, and HPV-52, which have higher oncogenic potential, were not found to be associated with koilocytes. These results were confirmed by HPV genotyping using microdissected koilocytes in 27 patients.Most common high-risk types belonging to α-9 and α-7 genotypes appear to rarely induce koilocytic changes. Therefore, koilocytes may provide additional useful information for predicting the risk of progression to high-grade lesions.

Diagnostic Role of Papanicolaou Smear, Hemoglobin, Blood Group, and Other Clinical Symptoms in Detecting Endometrial Carcinoma: A Clinicopathological Study of 175 Iranian Women with Endometrial Carcinoma.

Objectives:Endometrial carcinoma (EC) is a common gynecologic malignancy in the female genital tract, especially in postmenopausal women. The current study aimed to analyze Papanicolaou (Pap) smear in patients with EC to assess the relationship between EC and abnormal cells in Pap smear, ABO blood group, and hemoglobin anemia.Materials and Methods:A retrospective study was conducted on 175 patients with EC in Imam Hossein Hospital, Tehran, Iran, during the period from 2013 to 2019. The histology information of cases was extracted from the hospital database, and Pap smear slides were taken from pathological archives. The acquired information and slides were then reviewed by an expert pathologist in the hospital. The data were analyzed in SPSS (version 18) by the Chi-square test, Fisher's exact test, and independent-samples t-test. P < 0.05 was assigned as significant.Results:The mean age of patients was 54.47 ± 11.34, ranging from 32 to 85 years, and 31.4% of the patients were premenopausal women. Grade III, invasion ≥ 50% of myometrial, and advanced stage (III and IV) were diagnosed in 30%, 7.4%, and 22.86% of the patients, respectively. The most common histological types of EC were endometrioid (66.9%), papillary (10.3%), and malignant mixed Mullerian tumor (5.7%). The abnormal Pap smear was achieved only in 37 EC patients (21.1%). Endometrial cells, atypical squamous cells of undetermined significance, and atypical glandular cells were observed in 56.8%, 29.7%, and 13.5% of the patients > 40 years of age with abnormal Pap smears, respectively. In patients with normal Pap smears, the most common results were inflammation in 75.1% and atrophy in 14.6% of the patients. Abnormal uterine bleeding was the most important problem observed in patients with EC.Conclusion:The efficacy of Pap smear in detecting EC is limited. Therefore, women with EC, >40 years of age, with heavy bleeding, anemia, O blood group, inflammation, and atrophy in Pap smear in the past 3 years need to be precisely checked up for EC. These items can be added as new criteria for EC screening procedures.

Asymptomatic Bacterial Vaginosis in Cervical Cytology: A Preliminary Report of Incidental Finding in Routine Cervical Cancer Screening

Background: Bacterial vaginosis is a complex pathogenic group of bacteria which can cause a wide range of symptoms in affected women in Obstetric and Gynaecological practice. Its occurrence usually indicates alteration in the normal vaginal flora expected in majority of the women of reproductive age. Amongst the maintenance agents of the vaginal health, lactobacilli occupy a prominent place. Asymptomatic bacterial vaginosis is postulated to be linked to the development of cervical intraepithelial neoplasia by some researchers while some differ in this regard. Objective: To determine the rate of occurrence of asymptomatic bacterial vaginosis and associated factors in women who underwent cervical cytology for cervical cancer screening. Study Design: A cross sectional study involving microscopic examination of cervical smears of eligible clients presenting for routine Pap smear. Setting: The study was carried out in a well woman’s facility established and manned by female medical doctors in Enugu state with the assistance of trained nursing staff. Various female cancers and other communicable and non-communicable disease screening are performed at the centre. The services are available to the general public including various parts of the state and other nearby states. Methods: This preliminary study was carried out on women presenting for cervical cancer screening between February and July 2018 in a well woman’s centre in Enugu. Information on the socio-demographic and gynaecological history of the study participants were obtained and recorded in designated proformas. Smears that showed changes suggestive of bacterial vaginosis on microscopy were set aside for further evaluation. Such changes included: a conspicuous absence of normal flora of lactobacilli; filmy background of coccobacilli replacing the lactobacilli, individual squamous cells covered by a layer of bacteria (clue cells); viable squamous cells showing reactive changes identified as increased nuclear size, perinuclear halo and binucleation. Data entry and analysis were done using statistical package for social sciences (SPSS) computer software version 21.0. Results: 285 eligible women had cervical smear done during the first six months of the 2-year project at the centre. 50.9% were above reproductive age (>45 years), 38.2% were in second half of reproductive age (31 - 45), 10.9% were in the early reproductive age (15 - 30). 13% of the women were nulliparous, 51.9% were para 1 - 4, and 35.1% were grand multiparous. Of the 285 Pap smear slides viewed, 31 had bacterial vaginosis by the above criteria. This gave a prevalence of 10.8% in the reviewed cytological smears. There was an association of bacterial vaginosis with reproductive age. There was however, no association with age at coitarche and parity. There was also, no association of bacterial vaginosis with contraceptive use. There was no association between abnormal cervical cytology and asymptomatic bacterial vaginosis with absent lactobacilli. Conclusion: Prevalence of bacterial vaginosis is significant in our study population who were asymptomatic. Cervical smear should be taken advantage of as a tool for both cervical pre-cancer and bacterial vaginosis screenings since same sample can simultaneously be used for both conditions during cytology. Bacterial vaginosis may be unlikely involved in pathogenesis of cervical intraepithelial neoplasia. Recommendation: Researchers should harmonise the standards/criteria for the diagnosis of bacterial vaginosis because there are varied criteria for the diagnosis in the literature. Given, the dual advantage of pap smear in diagnosing cervical pre cancer stages and infections, more elaborate studies are needed to determine the usefulness of treatment or otherwise of bacterial vaginosis which constitutes significant incidental findings.

Diagnostic Classification of Cervical Cell Images from Pap Smear Slides

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.

Saliency-driven system models for cell analysis with deep learning

Background and objectives: Saliency refers to the visual perception quality that makes objects in a scene to stand out from others and attract attention. While computational saliency models can simulate the expert’s visual attention, there is little evidence about how these models perform when used to predict the cytopathologist’s eye fixations. Saliency models may be the key to instrumenting fast object detection on large Pap smear slides under real noisy conditions, artifacts, and cell occlusions. This paper describes how our computational schemes retrieve regions of interest (ROI) of clinical relevance using visual attention models. We also compare the performance of different computed saliency models as part of cell screening tasks, aiming to design a computer-aided diagnosis systems that supports cytopathologists.Method: We record eye fixation maps from cytopathologists at work, and compare with 13 different saliency prediction algorithms, including deep learning. We develop cell-specific convolutional neural networks (CNN) to investigate the impact of bottom-up and top-down factors on saliency prediction from real routine exams. By combining the eye tracking data from pathologists with computed saliency models, we assess algorithms reliability in identifying clinically relevant cells.Results:The proposed cell-specific CNN model outperforms all other saliency prediction methods, particularly regarding the number of false positives. Our algorithm also detects the most clinically relevant cells, which are among the three top salient regions, with accuracy above 98% for all diseases, except carcinoma (87%). Bottom-up methods performed satisfactorily, with saliency maps that enabled ROI detection above 75% for carcinoma and 86% for other pathologies.Conclusions:ROIs extraction using our saliency prediction methods enabled ranking the most relevant clinical areas within the image, a viable data reduction strategy to guide automatic analyses of Pap smear slides. Top-down factors for saliency prediction on cell images increases the accuracy of the estimated maps while bottom-up algorithms proved to be useful for predicting the cytopathologist’s eye fixations depending on parameters, such as the number of false positive and negative. Our contributions are: comparison among 13 state-of-the-art saliency models to cytopathologists’ visual attention and deliver a method that the associate the most conspicuous regions to clinically relevant cells.

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

BackgroundCervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images.MethodScene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm.ResultsThe evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab.ConclusionsThe major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm

Abstract Globally, cervical cancer ranks as the fourth most prevalent cancer affecting women. However, it can be successfully treated if detected at an early stage. The Pap smear is a good tool for initial screening of cervical cancer, but there is the possibility of error due to human mistake. Moreover, the process is tedious and time-consuming. The objective of this study was to mitigate the risk of mistake by automating the process of cervical cancer classification from Pap smear images. In this research, contrast local adaptive histogram equalization was used for image enhancement. Cell segmentation was achieved through a Trainable Weka Segmentation classifier, and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy c-means algorithm. The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and Pap smear slide images from a pathology unit). An overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%‘, ‘97.64%, 98.08% and 97.16%’ and ‘96.80%, 98.40% and 95.20%’ were obtained for each dataset respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that was utilized to select cell features that would improve the classification performance, and the number of clusters used during defuzzification and classification. The evaluation and testing conducted confirmed the rationale of the approach taken, which is based on the premise that the selection of salient features embeds sufficient discriminatory information that leads to an increase in the accuracy of cervical cancer classification. Results show that the method outperforms many of the existing algorithms in terms of the false negative rate (0.72%), false positive rate (2.53%), and classification error (1.12%), when applied to the DTU/Herlev benchmark Pap smear dataset. The approach articulated in this paper is applicable to many Pap smear analysis systems, but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

Automatic cell nuclei segmentation and classification of cervical Pap smear images

Abstract Pathological examination of microscopic image of Pap smear slide remains the main method for cervical cancer diagnosis. The accurate segmentation and classification of images are two important phases of the analysis. Firstly, the Mean-Shift clustering algorithm is applied to obtain regions of interest (ROI) for cell nuclei segmentation. Then the flexible mathematical morphology is applied to split overlapped cell nuclei for better accuracy and robustness. For classification of the images, features based on shape, textural features based on color space and Gabor features are extracted and put together to obtain better classification performance. The optimal feature set is obtained by chain-like agent genetic algorithm (CAGA), P-value and maximum relevance-minimum multicollinearity (MRmMC). The proposed segmentation and classification methods were tested on 362 cervical Pap smear images. Experimental results showed that the cervical cell nuclei can be segmented by the proposed segmentation method with high effective segmentation results (Sensitivity: 94.25%±1.03% and Specificity 93.45%±1.14%). The feature selection method based on CAGA with Gabor features has the highest classification performance for normal, uninvolved and abnormal images (more than 96% accuracy). The proposed method can automatically and effectively segment cell nuclei of microscopic images. From the experimental results, Gabor features and feature selection based on CAGA are apparently helpful for improving the performance of classification.

Inflammatory Cell Extraction in Pap smear Images: A Combination of Distance Criterion and Image Transformation Approach

In order to obtain a diagnosis of cervical cancer information, the characteristics of each cell nucleus must be identified and evaluated properly through a Pap smear test. The presence of inflammatory cells in Pap smear images can complicate the process of identification of cell nuclei in the early detection of cervical cancer. Inflammatory cells need to be eliminated to assist pathologists in reading Pap smear slides. In this work, we developed a novel method to extract the inflammatory cells that allow detection of cell nuclei more accuracy. The proposed algorithm consists of two stages: extraction of inflammatory cells using the distance criterion and image transformation. This experiment applied to the 1358 cells comprising 378 nuclei cells and 980 inflammatory cells from 25 Pap smear images. The results showed that our method can significantly reduce the amount of inflammation that can disrupt the cell nuclei in the detection process. The proposed method has promising results with a sensitivity level of 97% and a specificity of 84.38%.