scholarly journals Nominated Texture Based Cervical Cancer Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Edwin Jayasingh Mariarputham ◽  
Allwin Stephen
Keyword(s):  
Cervical Cancer ◽  
Pap Smear ◽  
Dynamic Range ◽  
Relative Size ◽  
Texture Features ◽  
Relative Displacement ◽  
University Hospital ◽  
Public Image ◽  
Support Vector ◽  
First Four Moments

Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of the seven classes. This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features) are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern histogram, tamura features, and edge orientation histogram. Few types of support vector machine (SVM) and neural network (NN) classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark, with 917 Pap smear images. The output of SVM is found to be best for the most of the classes and better results for the remaining classes.

scholarly journals Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap Smear Images

2020 ◽  
Vol 10 (5) ◽  
pp. 1800 ◽  
Author(s):  
Kyi Pyar Win ◽  
Yuttana Kitjaidure ◽  
Kazuhiko Hamamoto ◽  
Thet Myo Aung
Keyword(s):  
Image Processing ◽  
Cervical Cancer ◽  
Cancer Screening ◽  
Cervical Cancer Screening ◽  
Digital Image Processing ◽  
Digital Image ◽  
Pap Smear ◽  
Support Vector ◽  
Computer Assisted ◽  
Screening System

Cervical cancer can be prevented by having regular screenings to find any precancers and treat them. The Pap test looks for any abnormal or precancerous changes in the cells on the cervix. However, the manual screening of Pap smear in the microscope is subjective with poorly reproducible criteria. Therefore, the aim of this study was to develop a computer-assisted screening system for cervical cancer using digital image processing of Pap smear images. The analysis of Pap smear image is important in the cervical cancer screening system. There were four basic steps in our cervical cancer screening system. In cell segmentation, nuclei were detected using a shape-based iterative method, and the overlapping cytoplasm was separated using a marker-control watershed approach. In the features extraction step, three important features were extracted from the regions of segmented nuclei and cytoplasm. RF (random forest) algorithm was used as a feature selection method. In the classification stage, bagging ensemble classifier, which combined the results of five classifiers—LD (linear discriminant), SVM (support vector machine), KNN (k-nearest neighbor), boosted trees, and bagged trees—was applied. SIPaKMeD and Herlev datasets were used to prove the effectiveness of our proposed system. According to the experimental results, 98.27% accuracy in two-class classification and 94.09% accuracy in five-class classification was achieved using the SIPaKMeD dataset. When the results were compared with five classifiers, our proposed method was significantly better in two-class and five-class problems.

A Machine Learning Based Self-Risk Assessment Technique for Cervical Cancer

2020 ◽  
Vol 15 ◽  
Author(s):  
Zeeshan Ramzan ◽  
Muhammad Awais Hassan ◽  
H. M. Shahzad Asif ◽  
Amjad Farooq
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Cervical Cancer ◽  
Pap Smear ◽  
Demographic Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Screening Methods ◽  
Cervical Cancer Risk ◽  
Learning Techniques

Background: Cervical cancer is a highly significant cause of mortality in developing countries, and it is one of the most prominent forms of cancer worldwide. Machine learning techniques have been proven more accurate for the identification of cervical cancer as compared to the manual screening methods like Pap smear and Liquid Cytology Based (LCB) tests. Objective: Primarily, these machine-learning techniques use the images of the cervix for cervical cancer risk analysis, in this article, demographic data and medical records of patients are used to identify major causes of cervical cancer. Furthermore, normal classification methods are used as a usual way of classification when the dataset is balanced as this dataset has abundant examples of negative cases as compare to positive cases Then, traditional binary class classifiers are not sufficient to classify the examples of cervical cancer correctly. Methods: We identified the major causes of cervical cancer by employing multiple machine learning feature selection algorithms. After this selection, we trained different machine learning methods including Decision Trees (DTs), Support Vector Machines (SVMs) and Ensemble Learners using all features as well as these important features. Results and Conclusion: AdaBoost is able to classify instances into healthy and unhealthy classes of this unbalanced dataset with 96% accuracy. Based on this model and significant causes of cervical cancer, we aimed to develop a technique for self-risk assessment of cervical cancer, which women can use to know their chances of being infected from cervical cancer after answering some questions about their demographics and medical history.

scholarly journals Classification of Cervical Cancer from MRI Images using Multiclass SVM Classifier

2018 ◽  
Vol 7 (2.25) ◽  
pp. 1
Author(s):  
Bethanney Janney.J ◽  
Umashankar G ◽  
Sindu Divakaran ◽  
Shelcy Mary Jo ◽  
Nancy Basilica.S
Keyword(s):  
Cervical Cancer ◽  
Region Of Interest ◽  
Region Growing ◽  
Texture Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Main Body ◽  
Birth Canal ◽  
Abnormal Growth ◽  
Multiclass Svm

Cervical Cancer is the abnormal growth of tissues in the lower, narrow part of the uterus (womb) called the Cervix which connects the main body of the uterus, to the vagina or birth canal. Cervical cancer is one of the most common types of cancer that can be seen in women worldwide. Early detection and proper diagnosis can prevent the severity level and reduce the death rates .In this paper, we have proposed an automated diagnosis system of cervical cancer using texture features and Multiclass SVM (Support Vector Machine) Classifier in MRI images. Initially the MRI images are pre-processed to remove undesirable noises and other effects. After pre-processing, the image is segmented by Region growing method to obtain the region of interest. Texture features are extracted from the segmented region. Almost 22 features are extracted at the region of a segmented area and then passed on to Multiclass SVM Classifier to detect if the given image is cancerous or not. The results of the proposed techniques provide effective results for classifying cancerous and the non-cancerous image. 

scholarly journals Knowledge, attitude and behaviors of the mothers of 10-15 year old daughters regarding cervical cancer and HPV vaccine 10-15 yaş arası kız çocuğu olan annelerin rahim ağzı kanseri ve HPV aşısı konusunda bilgi, tutum ve davranışları

2018 ◽  
Vol 15 (2) ◽  
pp. 1072 ◽  
Author(s):  
Meltem Kürtüncü ◽  
Nurten Arslan ◽  
Işın Alkan ◽  
Özgür Bahadır
Keyword(s):  
Cervical Cancer ◽  
Hpv Vaccine ◽  
Pap Smear ◽  
Hpv Vaccination ◽  
English Summary ◽  
University Hospital ◽  
Pap Smear Test ◽  
Smear Test ◽  
Significant Difference ◽  
And Behaviors

This study was performed to determine the knowledge, attitude and behaviors of the mothers of 10-15 year old daughters regarding cervical cancer and HPV vaccination. This was a descriptive and sectional study. 100 mothers among the ones who admitted to the polyclinic to a university hospital who approved to participate in the study were included in the study. Data collection form was prepared by the researcher and collected by face-to-face interview technique. Chi-Square test was used in statistical analyse. It was observed that 47% of the mothers have not heard about HPV but about 67% of mothers HPV vaccination. It was seen that 91% have known that HPV caused cervical cancer and 88% of the mothers have heard pap smear test, but 56% have not undergone the test. 88% of the mothers have heard pap smear test, but 56% have not undergone the test. 15% of the mothers wanted to be informed about the reliability, 9% of side effects, 14% protection level and 12% of protection duration of the vaccine. When education status of the mothers and whether they give information to their children about health issues were examined, it was increased about hearing HPV vaccine and there was a signifant difference about giving knowledge rate their children. And also, there was a significant difference about giving knowledge, especially general health status, and knowledge level of mothers who were working. In conclusion, it should be provided to plan and disseminate education programs for the mothers about cervical cancer, HPV and HPV vaccine. Extended English summary is in the end of Full Text PDF (TURKISH) file.  ÖzetAraştırma 10-15 yaş arası kız çocuğu olan annelerin rahim ağzı kanseri ve HPV aşısı hakkında bilgi, tutum, davranışlarını belirlemek amacıyla yapıldı. Araştırma tanımlayıcı ve kesitsel tiptedir. Araştırmaya bir üniversite hastanesinin polikliniğine başvuran ve 10-15 yaş arası kız çocuğu olan 100 anne alındı.  Veriler araştırmacı tarafından hazırlanan katılımcı bilgi formuyla yüz yüze görüşme yöntemiyle toplandı. İstatiksel analizde ki-kare testi kullanıldı. Annelerin %47’sinin HPV enfeksiyonunu daha önce duymadığı ancak %67’sinin HPV aşısını daha önce duyduğu görüldü. Annelerin %91’inin HPV’ nin rahim ağzı kanserine neden olduğunu bildiği görülürken, %88’inin rahim ağzı kanserini ve papsmear testini duyduğu, ancak %56’sının pap smear testini yaptırmadığı belirlendi. Annelerin %15’i aşının güvenilirliği, %9’u yan etkileri, %14’ü koruyuculuk düzeyi, %12’si koruma süresi hakkında bilgilendirilmek istediğini ifade etti. Annelerin eğitim durumlarına göre çocuklarına sağlık konusunda bilgi verip vermediklerine bakıldığında annelerin eğitim seviyesi arttıkça HPV aşısını duyma oranlarının artışı ve çocuklarını bilgilendirme oranları arasında istatistiksel olarak anlamlı fark saptanmıştır. Çalışan annelerin özellikle genel sağlık ile ilgili konularda bilgi verme ve HPV ile ilgili bilgi durumları arasında da anlamlı fark bulunmuştur. Sonuç olarak, annelere rahim ağzı kanseri, HPV ve HPV aşısı ile ilgili eğitim programlarının planlanması ve bilgi durumlarının artması için de yaygınlaştırılması önerilmektedir.

scholarly journals Single-cell conventional pap smear image classification using pre-trained deep neural network architectures

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohammed Aliy Mohammed ◽  
Fetulhak Abdurahman ◽  
Yodit Abebe Ayalew
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Computer Vision ◽  
Single Cell ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Pap Smear ◽  
Experimental Result ◽  
Network Architectures ◽  
Average Accuracy

Abstract Background Automating cytology-based cervical cancer screening could alleviate the shortage of skilled pathologists in developing countries. Up until now, computer vision experts have attempted numerous semi and fully automated approaches to address the need. Yet, these days, leveraging the astonishing accuracy and reproducibility of deep neural networks has become common among computer vision experts. In this regard, the purpose of this study is to classify single-cell Pap smear (cytology) images using pre-trained deep convolutional neural network (DCNN) image classifiers. We have fine-tuned the top ten pre-trained DCNN image classifiers and evaluated them using five class single-cell Pap smear images from SIPaKMeD dataset. The pre-trained DCNN image classifiers were selected from Keras Applications based on their top 1% accuracy. Results Our experimental result demonstrated that from the selected top-ten pre-trained DCNN image classifiers DenseNet169 outperformed with an average accuracy, precision, recall, and F1-score of 0.990, 0.974, 0.974, and 0.974, respectively. Moreover, it dashed the benchmark accuracy proposed by the creators of the dataset with 3.70%. Conclusions Even though the size of DenseNet169 is small compared to the experimented pre-trained DCNN image classifiers, yet, it is not suitable for mobile or edge devices. Further experimentation with mobile or small-size DCNN image classifiers is required to extend the applicability of the models in real-world demands. In addition, since all experiments used the SIPaKMeD dataset, additional experiments will be needed using new datasets to enhance the generalizability of the models.

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