Classification of Clinical Dataset of Cervical Cancer using KNN

Sharma, Meenakshi; Singh, Sanjay Kumar; Agrawal, Prateek; Madaan, Vishu

doi:10.17485/ijst/2016/v9i28/98380

Cited by 52 publications

(19 citation statements)

References 7 publications

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“…Therefore, the texture feature was extracted using GLCM (Gray Level Co-Occurrence Matrix) in our feature extraction stage. The mostly used classifiers in the multi-cell cervical image analysis are support vector machine (SVM) [27], LDA (Linear Discriminant Analysis) [28], k-nearest neighbor (KNN) [29], and ANN (Artificial Neural Networks) [30]. There have been many research studies about cervical cancer detection, but most studies have only targeted the segmentation of nuclei regions [31].…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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“…In most patients with cervical cancer, a high level of accuracy is not obtained when determining the stage of the disease. This problem has been approached from a computational perspective [2,3,4,7,10,13,17], however, based on the analysis of the state of the art, it has been identified that: the percentages of correct classification of stages of the disease can still be improved.…”

Section: Problematicmentioning

confidence: 99%

“…Images segmentation was made for detect contours and to detect nuclei and cytoplasm of cells, once isolated they extracted morphological characteristics as area, perimeter, extension, and nucleus ratio with cytoplasm. Subsequently, the KNN algorithm was used as classier, obtaining 84.3% of accuracy [17].…”

Section: Previous Workmentioning

confidence: 99%

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Classification of Cervical Cancer Using Assembled Algorithms in Microscopic Images of Papanicolaou

Gómez¹,

Sánchez-DelaCruz²,

Mata³

2017

RCS

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In Mexico cervical cancer is the second leading cause of death from malignant neoplasms in women, but this mortality rate has been reduced in recent years thanks to early detection programs as the pap smear test, which is aimed at finding pre-cancerous abnormalities in cells that cover the cervix. The pap smear test is an efficient medical test, but it presents problems at the moment of interpretation under the microscope, due to the large number of cells in the sample and others external factors. In order to solve this disadvantage, computational techniques are used to support the samples classification. In this research we propose to use assembled algorithms to construct a classifier. The database used is from Herlev University Hospital, the data were formulated as a binary classification problem. The results of the experiments (exhaustive search) show that using the combinations of algorithms Bagging+MultilayerPerceptron and AdaBoostM1+LMT is obtained a high percentage of correctly classified instances, 95.74%.

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“…In the past decade, various machine learning approaches and CAD systems have been used for detection and classification of abnormal cell images such as K-nearest neighbors [6,7], support vector machines (SVM) [7,8], artificial neural networks (ANN) [9] etc. Plissiti et al [10] applied K-PCA (Gaussian kernel) method to classify cervical cells images to normal and abnormal classes using nucleus features by ignoring the features derived from the cytoplasm.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Classification of Cervical Cell Images from Pap Smear Slides

Talo¹

2019

acperpro

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Analysis of pap smear images under a microscope by experts is a laborious and time-consuming task. Computer aided 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 to classify cervical cell images 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 promising results as compared to the previous studies in the literature. The proposed deep learning model can give a second opinion to clinicians in their daily routines and help them to focus on more complex cases.

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Classification of Clinical Dataset of Cervical Cancer using KNN

Cited by 52 publications

References 7 publications

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

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

Classification of Cervical Cancer Using Assembled Algorithms in Microscopic Images of Papanicolaou

Diagnostic Classification of Cervical Cell Images from Pap Smear Slides

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