New Abnormal Cervical Cell Detection Method of Multi-Spectral Pap Smears

Considering the problem of traditional cervical cancer detection method that brings high false negative rate （FNR） and high false positive rate （FPR）, a new abnormal cervical cells detection method of multi-spectral Pap smear is proposed in this thesis, on the basis of multi-spectral microscopic imaging technology and computer automotive recognition technology. At first, image in a specific wave band is segmented according to the relationship between intensity and spectrum of each pixel. Then, multi-spectral features of each pixel are extracted making use of improved cosine correlation analysis （CCA） algorithm. Combined with the characteristic of each cell＇s area, final definition is made. Experiments have proved the new approach could identify abnormal cells efficiently as well as lower FNR and FPR.

Perceived Barriers to Cervical Cancer Screening Using Pap Smear Test among Women Attending Saad Abu Al Ella Hospital in Khartoum State, 2022

Background: Cervical cancer is the second common cancer among women worldwide. It is a preventable cancer, and early detection of precancerous conditions through the Papanicolaou cytology screening (Pap smear) is a key aspect of prevention;it is accepted worldwide as an efficient tool for secondary prevention. While the PS test is simple, inexpensive, and relatively reliable as a method of diagnosing cervical cancer, most women do not take the test. Therefore, this study is sought to describe the barriers to pap smear uptake among Sudanese women. Materials and Method: This total coverage observational, analytical and cross sectional, hospital-based study was conducted in Saad Abu El Ella Hospital in April 2022. The study was conducted using an anonymous questionnaire to assess the perceived barriers of 93 participants. All data were computerized using Microsoft Excel’17 and the data were described and analyzed using statistical package for social science (SPSS23). Results: The findings revealed that the mean age of the participants was 39.5 years and only 3.2% had ever undergone a pap smear test. Identified barriers were lack of information, not knowing where to go, and fear of pain. The majority, 72% are willing to routinely perform a pap smear test if well informed about it. The study also demonstrates that there is a significant correlation between perceived barriers score and willingness to perform the pap smear test (p value = 0.008), and between the perceived barriers score and the sociodemographic factors: Age (p value = 0.006), educational level (p value = 0.028) and occupation (p value = 0.040), but no association with the economic status was found (p value = 0.378). Conclusion: The detection rate is too low compared to the national target of over 70%. Therefore, more work is needed to reduce perceived barriers to cervical cancer screening by providing education/raising for popular awareness;addressing misconceptions and false beliefs;informing women about the necessity and importance of Pap smear;and health promotion using mass media such as national television, social media, radio, billboards, and newspapers and other print media.

Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues.Biomedical image processing concepts are identical to biomedical signal processing,which includes the investigation,improvement,and exhibition of images gathered using x-ray,ultrasound,MRI,etc.At the same time,cervical cancer becomes a major reason for increased women’s mortality rate.But cervical cancer is an identified at an earlier stage using regular pap smear images.In this aspect,this paper devises a new biomedical pap smear image classification using cascaded deep forest(BPSIC-CDF)model on Internet of Things(IoT)environment.The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition.In addition,the pre-processing of pap smear images takes place using adaptive weighted mean filtering(AWMF)technique.Moreover,sailfish optimizer with Tsallis entropy(SFO-TE)approach has been implemented for the segmentation of pap smear images.Furthermore,a deep learning based Residual Network(ResNet50)method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images.In order to showcase the improved diagnostic outcome of the BPSICCDF technique,a comprehensive set of simulations take place on Herlev database.The experimental results highlighted the betterment of the BPSICCDF technique over the recent state of art techniques interms of different performance measures.

Cervical Cancer Prevention Challenges and Barriers to Cervical Cancer Screening and HPV Vaccinations in Ukraine and Eastern Europe

Objectives: To identify the obstacles and issues that hinder effective cancer prevention efforts in Ukraine. The study aims to provide a comprehensive assessment of the barriers to cancer prevention, including both infrastructure and behavioral factors, and identify potential solutions to address these challenges. Study Design: Comprehensive literature review. Methods: The following databases were searched: National Center for Biotechnology Information (NCBI) and PubMed (U.S. National Library of Medicine at the National Institutes of Health). The keywords used in the search included “Cervical Cancer”, “Human Papillomavirus Vaccination (HPV)”, “Ukraine”, “Eastern Europe”, “Healthcare Infrastructure in Disasters”, “Cervical Cancer Prevention”, “Pap Smear”, and “HPV Testing”. Results: A total of 3500 articles were screened. A total of 65 articles met the inclusion criteria. Limited public awareness and limited access to vaccination and screening, combined with inadequate treatment facilities lead to higher rates of cervical cancer. The COVID pandemic, war with Russia, and the Chernobyl disaster are significant factors for the low level of vaccination in Ukraine. Conclusion: The prevention and treatment of cervical cancer in Ukraine face significant challenges due to the inadequate HPV vaccination rates and screening by cytology. Efforts to improve funding and increase education of both the population and health care providers are necessary to increase interventions such as HPV vaccination, cervical cytology, and HPV testing to reduce cervical cancer rates in Ukraine.

Expectations and Perspectives of Users with the Screening Program for Cervical Cancer

Objective: Evaluate user satisfaction before and after taking the Pap. Methodology: Observational and prospective study. It was carried out in two health centers in San Luis Potosicity, Mexico in 93 users which attended for a Pap test from February to June 2015. The satisfaction was assessed using multidimensional scaling SERVQUAL whit 5 dimensions to evaluate the quality of services in an organization: reliability, responsibility, security, empathy and touchable and materials goods (tangible elements). This scaling was obtained internal consistency by Cronbach’s alpha coefficient with a value of 0.74. For data analysis, nonparametric test Wilcoxon for related samples was used. Results: In all cases, perspective was less than expectancy where the means of the scores before attention (expectation) and after care (perspective) there was a statistically significant difference (p = 0.000). Before receiving care users had an expectation of 192.96 points, after care perspective score dropped to 184.49 points indicating that the care provided was not what they expected. The difference in scores was 8.47 points (p = 0.004). Conclusions: Users of the screening program for cervical cancer were unsatisfied because the attention was not what they expected. In most of the indicators studied, a high percentage of dissatisfaction was obtained.

Capture of cervical exfoliative cells on a glass slide coated by 3-glycidyloxypropyl trimethoxysilane and poly-L-lysine

A new modification method for glass slides was developed and applied to make ThinPrep Pap smears,in order to increase the adhesion ability of cervical exfoliative cells.3-glycidyloxypropyl trimethoxysilane(GOPS) was coated on the glass slides firstly on the slides,then poly-L-lysine(PLL) was covalently modified onto the above epoxy-terminated slides to form GOPS-PLL double decorated slides.The modified slides were characterized using X-ray photoelectron spectroscopy(XPS) and atomic force microscopy(AFM).The cell adhesion ability effect was tested and compared with traditional PLL coated slides by fixing the cervical exfoliative cells on the double adorned slides.The control test was conducted by the bare glass slides unmodified.The cell morphology of cervical exfoliative cells adhered on different slides was observed under the microscope after Papanicolaou staining.The number of cervical exfoliative cells on the unmodified slides,PLL coated slides and GOPS-PLL coated slides was 10307300,32837226 and 41197280(n=12),respectively.The data among the three different modification methods showed significant differences(one-way analysis of variance,ANOVA test,Po0.05).The cell capturing effect of the GOPS-PLL slide was the best among the three different modified slides.In addition,the GOPS-PLL slide could enhance the uniformity of the adhered cells and be widely applied to the ThinPrep system for cervical carcinoma screening to increase the accuracy rate of diagnosis.

Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

Cervical cancer is screened by pap smear methodology for detection and classification purposes.Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues.In this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes problem.Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells.Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)classifier.The success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%sensitivity.On top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high accuracy.On the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%accuracy.The proposed system is a built-in cascading manner with five models of polynomial(SVM)classifier.The overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases.An important kind of biomedical image is Pap smear image that iswidely employed for cervical cancer diagnosis.Cervical cancer is a vital reason for increased women’s mortality rate.Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer.Computer-aided systems for cancerous cell detection need to be developed using deep learning(DL)approaches.This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification(IDCNN-CDC)model using biomedical pap smear images.The proposed IDCNN-CDC model involves four major processes such as preprocessing,segmentation,feature extraction,and classification.Initially,the Gaussian filter(GF)technique is applied to enhance data through noise removal process in the Pap smear image.The Tsallis entropy technique with the dragonfly optimization(TE-DFO)algorithm determines the segmentation of an image to identify the diseased portions properly.The cell images are fed into the DL based SqueezeNet model to extract deeplearned features.Finally,the extracted features fromSqueezeNet are applied to the weighted extreme learning machine(ELM)classification model to detect and classify the cervix cells.For experimental validation,the Herlev database is employed.The database was developed at Herlev University Hospital(Denmark).The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity,specificity,accuracy,and F-Score.

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.

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images.Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist.Therefore,automated cervical cancer diagnosis using automated methods are necessary.This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis(ODLIM-CCD)using pap smear images.The proposed ODLIM-CCD technique incorporates median filtering(MF)based pre-processing to discard the noise and Otsu model based segmentation process.Besides,deep convolutional neural network(DCNN)based Inception with Residual Network(ResNet)v2 model is utilized for deriving the feature vectors.Moreover,swallow swarm optimization(SSO)based hyperparameter tuning process is carried out for the optimal selection of hyperparameters.Finally,recurrent neural network(RNN)based classification process is done to determine the presence of cervical cancer or not.In order to showcase the improved diagnostic performance of the ODLIM-CCD technique,a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.

Study of Prevalence of Abnormal Cervical Cytology in Al-Shatby Maternity University Hospital

Introduction: Cervical cancer is the most common cause of preventable cancer related deaths;cervical cancer has a long pervasive phase (cervical dysplasia);the prevalence of cervical dysplasia varies according to the socioeconomic characteristics and geographic areas of the population studied. Low-grade lesions regress spontaneously in a significant number of patients, while high grade lesions will progress to an invasive cancer if left untreated. Cervical cancer screening is an important component of the World Health Organization (WHO) strategy for combating cervical cancer. The incidence and prevalence of cervical cancer has reduced remarkably over the last three decades in developed countries where there are effective, well-coordinated screening programs, and treatment of cervical dysplasia, while in developing countries it has been increasing and has constituted major health problems among women where there are no well-coordinated and effective screening programs, also resources are very low and no insurance can cover this programs. Aim of the work: The aim is to assess the prevalence of abnormal cervical cytology in Al Shatby Maternity University Hospital patients using Pap smear. Materials and methods: Inclusion criteria: 1) Married woman from 3 years or more;2) Women age from 21 to age 65 years. Exclusion criteria: 1) Previously known cervical cancer patient;2) Virgin females;3) Woman with active vaginal bleeding. Results: 83% of patients were -ve intraepithelial neoplasia {37.7% was normal cytology and 45.3% was inflammatory}. 17% was +ve intraepithelial neoplasia (abnormal cytology), {11.1% ASCUS, 2.9% LSIL, 1.3% HSIL, 1.1% ASC-H, 0.3% AGS-NO, 0.3% AGS-Favour Neoplastic}. Prevalence of abnormal cervical cytology in age group less than30 years was 8.4% which is lower than prevalence in the middle age group which was 19.9%. Prevalence of abnormal cervical cytology in women with normal vaginal delivery was higher than those with caesarean delivery. 39.8% of our patients were passive and active smokers 61.2% of their Pap smear was abnormal cytology. 78.9% of abnormal cytology was among patients from low socioeconomic class (rural areas). Abnormal cervical cytology in patients with high parity was 69% which is higher than abnormality found in lower parity. 60.2% of abnormal cervical cytology was in patients who became sexually active before age of 20 years. Prevalence of abnormal cervical cytology was higher in patients with multiple sexual partners (56.5%) than patients with single sexual partner (13.3%). Conclusion: Cervical cytology remains the gold standard for cervical cancer screening and the use of Bethesda system is a simple and accurate method for diagnosis and management of cases with abnormal cervical cytology.

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.

Papanicolaou smear interpretation with progestin-only hormonal birth control methods

Objective To define the relationship between the use of progestogen-only contracep- tion and the occurrence of abnormal cervical cytology and cervical intraepithelial lesion. Methods The present study was designed as a case-control study including 200 progestogen-only contraceptive （POC） users recruited as cases （group A） and 50 women who were non-hormonal contraceptive users as a control group （group B）. Positive Papanicolau （Pap） smear was reported in cervical cell abnormalities from atypical squamous cell of undetermined significance （ASCUS） to invasive cancer. Pap smear was evaluated according to the Bethesda system classification 2001. Cases with posi- tive Pap smears were subjected to visual inspection of the cervix with 5.0% acetic acid （VIA test） and punch biopsy. Results Statistical analysis of Pap smear results in this study showed epithelial cell abnormalities in 44 cases （22.0%） in group A and 9 cases （18.0%） in group B showing no statistically significant difference （P〉O. 05）. ASCUS abnormality was reported in 31 cases （15.5%） of group A and 6 cases of group B （12.0%）. Low-grade squamous intraepithelial lesion （LSIL） was found in 11 cases （5.5%） and 3 cases （6.0%） of group A and group B, respectively. High-grade squamous intraepithelial lesion （HSIL） was found in 2 cases （1.0%） of group A and none of group B. The incidence of abnormal Pap smears in group A did not greatly vary between different methods of POCs. Among Depo medroxyprogesterone acetate （DMPA） users, ASCUS was found in 17 cases （16.8%）, LSIL was found in 11 cases （10.8%） and HSIL was found in 2 cases （1.9%）. While in progesterone-only pills （POPs） users and Implanon~ users, ASCUS was found in 10 cases （12.8%） and 4 cases （20.0%） respectively with no higher epithelial abnormalities found. Levonorgestrel intrauterine device （LNG-IUD） was used by 1 case in group A and was not associated with Pap smear abnormality. Cases with ASCUS abnormality were followed up by repeated Pap smear after 3 months. Twenty cases （64.5%） and 3 eases （50.0%） of groups A and B, respectively showed complete regression to normal. Eleven cases （35.5%） and 3 cases （50.0%） of groups A and B respectively showed persistent features of ASCUS. Punch biopsy was done in eases with higher grade epithelial lesions （LSIL ＆ HSIL） showing 4 cases （30.8%） with mild dysplasia or CIN I, 2 cases （15.4%） with moderate dysplasia in group A, 2 cases （66. 7%） with mild dysplasia and I case （33.3%） with moderate dysplasia in group B. Seven cases （53.8%） of group A showed cytological abnormality without concurrent dysplasia seen in histopathological examination. None of the included eases in this study were active smokers, while in group A passive smoking was found in 44 cases with abnormal Pap smear （100.0%） including 31 ASCUS cases （100.0%）, 11 LSIL cases （100.0%） and 2 HSIL cases （100.0%）. In group B passive smoking was found in 9 cases with Pap smear abnormality including 6 ASCUS cases （100. 0%）, 3 LSIL cases （100. 0%）. There was a statistically significant difference between cases with normal and abnormal Pap smears regarding smoking status. Conclusion POCs use is not associated with increased risk of abnormal cytologic findings and preinvasive disease of the cervix. Smoking including passive smoking has a positive association with abnormal Pap smears. The use of the Bethesda system for cytological interpretation is a simple and accurate method for management of abnormal cytology.