Automated cervical cancer detection through RGVF segmentation and SVM classification

Sajeena, T A; Jereesh, A S

doi:10.1109/coconet.2015.7411260

Cited by 27 publications

(9 citation statements)

References 21 publications

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“…SVM classifiers are used to validate the accuracy of classification 24,51‐53 . The Kernel used for the classification is represented as in Equation )

k ((), x, y) = e^{- \frac{{((), ∥ x - y ∥)}^{2}}{2 σ^{2}}}

where σ denotes the kernel width of the Gaussian kernel.…”

Section: Methodsmentioning

confidence: 99%

Automatic segmentation of nuclei from pap smear cell images: A step toward cervical cancer screening

Devaraj¹,

Gnanasaravanan²,

Nirmala³

2020

Int J Imaging Syst Tech

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Cervical cancer is identified as the fourth most recurrent cancer among women across the globe. The cancer is treatable, if identified at the early stage. Pap smear test is the most common and the best tool for initial screening of cancer. Pap smear cell level image analysis is an open issue. The limitation of the analysis is due to the complexity of the cell structure. The smear cell image is composed of cytoplasm and nucleus. The shape and structure of the nucleus determines the cancer prevalence. Segmentation of nucleus is an important step in cancer detection. There are various methods developed for nucleus segmentation. The article proposes multithresholding algorithm to segment cytoplasm and nucleus region from the background. Morphological operations are used for correcting the segmented output. Support vector machine classifier is used for classifying the smear cell as normal or abnormal based on the extracted features of the segmented output. The obtained accuracy of the classifier, sensitivity and specificity for single smear cell are 99.66%, 99.85% and 99.17%.

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“…SVM classifiers are used to validate the accuracy of classification 24,51‐53 . The Kernel used for the classification is represented as in Equation )

k ((), x, y) = e^{- \frac{{((), ∥ x - y ∥)}^{2}}{2 σ^{2}}}

where σ denotes the kernel width of the Gaussian kernel.…”

Section: Methodsmentioning

confidence: 99%

Automatic segmentation of nuclei from pap smear cell images: A step toward cervical cancer screening

Devaraj¹,

Gnanasaravanan²,

Nirmala³

2020

Int J Imaging Syst Tech

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“…Literature [52] collected a total of 121 features comprising 5 shape descriptor, 50 textures, 66 ripplet descriptors and applied an ensemble classification using weighted majority voting. Literature [53] divided the image into blocks with certain size and extracted the texture and color histogram features which show significant differences between blocks with and without suspicious cells, then these features are input into the support vector machine for classification. Literature [54] used Radiating Gradient Vector Flow Snake to segment the single cervical image into cytoplasm, nucleus and background, then cellular and nuclei features are extracted for the training of Support Vector Machine (SVM), artificial neural networks (ANN) and Euclidean distance-based system to classify seven different types of cells.…”

Section: ) Comparison Of Feature Selection Methodsmentioning

confidence: 99%

“…Literature [55] used the fuzzy C-means (FCM) to segment the single cell images and tested on 5 classifiers including Bayesian classifier, linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural networks (ANN), and support vector machine (SVM) with nucleus-based and cell-based features. Among the 4 methods, the accuracy of literature [53] is 80.92% and the number of features is 11. The recognition accuracy of this paper is 0.4% higher and the number of features is reduced by 2.…”

Section: ) Comparison Of Feature Selection Methodsmentioning

confidence: 99%

A Parametric Optimization Oriented, AFSA Based Random Forest Algorithm: Application to the Detection of Cervical Epithelial Cells

Jia

Zhang

2020

IEEE Access

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Cervical cancer is one of the most common cancers among women in the world, over 570,000 patients are affected annually. Pathological examination for patients using Pap Smear becomes the mainstream of cervical cancer diagnoses. Accurate diagnoses and analyses largely rely on 3 factors: cell segmentation, feature extraction and selection as well as classification. Firstly, a 2-layer segmentation algorithm based on block Maximum Between-Class Variance (Otsu) and Gradient Vector Flow (GVF) Snake model is applied to obtain regions of interest (ROI). Then the features of chroma, shape and texture are extracted and selected for a better classification performance. The random forest algorithm based on Artificial Fish Swarms Algorithm (AFSA) is used to recognize and classify cervical epithelial cells. The proposed methods were tested on 200 cervical Pap Smear images. Experimental results show that cervical cells can be segmented with an effective segmentation result. The proposed feature selection method achieved an accuracy of 81.31% with the minimum feature number. The improved random forest algorithm with 2 and 7 classification under fivefold cross-validation reaches the highest classification accuracy (96.86%). Experimental results showed that the proposed method has obvious recognition advantages, and thus provides a practical classification frame for the diagnoses of cervical cancer. INDEX TERMS Artificial fish swarm algorithm, random forest, cell detection, image segmentation.

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“…Many automated [7][8][9][10][11][12][13] and semi-automated [14] systems have been proposed in the last 10 years. Some have worked on a single cell, and some have worked on multiple cells.…”

Section: Introductionmentioning

confidence: 99%

Texture‐based feature extraction of smear images for the detection of cervical cancer

Arya

Mittal

Singh

2018

IET Computer Vision

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In India, cervical cancer is the second most common type of cancer in females. Pap smear is a simple cytology test for the detection of cancer in its early stages. To obtain the best results from the Pap smear, expert pathologist are required. Availability of pathologist in India is far below the required numbers, especially in rural parts. In this paper, multiple texturebased features are introduced for the extraction of relevant and informative features from single-cell images. First-order histogram, GLCM, LBP, Laws, and DWT are used for texture feature extraction. These methods help to recognise the contour of the nucleus and cytoplasm. ANN and SVM are used to classify the single-cell images either normal or cancerous based on the trained features. ANN and SVM are used on every single feature as well as on the combination of all features. Best results are obtained with a combination of all features. The system is evaluated on generated dataset MNITJ, containing 330 single cervical cell images and also on publicly available benchmark Herlev data set. Experimental results show that the proposed texturebased features give significantly better results in cervical cancer detection when compared with state of the art shape-based features regarding accuracy.

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Automated cervical cancer detection through RGVF segmentation and SVM classification

Cited by 27 publications

References 21 publications

Automatic segmentation of nuclei from pap smear cell images: A step toward cervical cancer screening

Automatic segmentation of nuclei from pap smear cell images: A step toward cervical cancer screening

A Parametric Optimization Oriented, AFSA Based Random Forest Algorithm: Application to the Detection of Cervical Epithelial Cells

Texture‐based feature extraction of smear images for the detection of cervical cancer

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