Intelligent Screening Systems for Cervical Cancer

Jusman, Yessi; Ng, Siew‐Cheok; Osman, Noor Azuan Abu

doi:10.1155/2014/810368

Cited by 58 publications

(48 citation statements)

References 116 publications

(219 reference statements)

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“…Neural networks, support vector machines (SVM), k-Nearest Neighbors (KNN), linear discriminant analysis (LDA), and decision trees are commonly used for studying cervical cancer [11]. Kim et al [2] applied a linear SVM to classify Cervigrams into CIN1/normal or CIN2/3+, while Song et al [7] utilized KNN coupled with a majority voting algorithm to perform the CIN classification.…”

Section: Related Workmentioning

confidence: 99%

Multi-feature based benchmark for cervical dysplasia classification evaluation

Zhang

Xin

et al. 2017

Pattern Recognition

101

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Cervical cancer is one of the most common types of cancer in women worldwide. Most deaths due to the disease occur in less developed areas of the world. In this work, we introduce a new image dataset along with expert annotated diagnoses for evaluating image-based cervical disease classification algorithms. A large number of Cervigram® images are selected from a database provided by the US National Cancer Institute. For each image, we extract three complementary pyramid features: Pyramid histogram in L*A*B* color space (PLAB), Pyramid Histogram of Oriented Gradients (PHOG), and Pyramid histogram of Local Binary Patterns (PLBP). Other than hand-crafted pyramid features, we investigate the performance of convolutional neural network (CNN) features for cervical disease classification. Our experimental results demonstrate the effectiveness of both our hand-crafted and our deep features. We intend to release this multi-feature dataset and our extensive evaluations using seven classic classifiers can serve as the baseline.

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Section: Related Workmentioning

confidence: 99%

Multi-feature based benchmark for cervical dysplasia classification evaluation

Zhang

Xin

et al. 2017

Pattern Recognition

101

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“…The accepted ones include Artificial Neural Network (ANN), Support Vector Machine (SVM), Logistic Regression, K-Nearest neighbour, Linear Discriminant Analysis (LDA), and Decision Tree [11].…”

Section: Issn(e): 2277-128x Issn(p): 2277-6451 Doi: 1023956/ijarcsmentioning

confidence: 99%

Segmentation of Cervical Cells for Automated Screening of Cervical Cancer: A Review

2017

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Abstract--In automatic screening of cervical cytology, the morphological features of cell play a determining role. To avoid false diagnosis,exigency to precisely extraction these features led to emergence of new segmentation models. In this paper author aspire to present literature review of research done in the field of segmentation stage in automatic screening of cervical smear images. Total of 35 publications are considered for the time period of 10 years. A detailed study of segmentation technique proposed in each publication is considered, which presents a chronological development and upgradation of segmentation models. This review assist researcher to have thorough knowledge of various state-of-art segmentation models and the problems and complexities required to be tackled, for unambiguous determination of malignancies in cervical cytology.

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“…and tissue-level approach (i.e., colposcopy, cervicography, etc.) 2 which were developed based on analysis of the images. In the screening of cervical cancer, the abnormality of cells is investigated based on the morphological component of cells such as nucleus and cytoplasm.…”

Section: Introductionmentioning

confidence: 99%

A system for detection of cervical precancerous in field emission scanning electron microscope images using texture features

Jusman

Hasikin‬

et al. 2017

J. Innov. Opt. Health Sci.

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This study develops a novel cervical precancerous detection system by using texture analysis of¯eld emission scanning electron microscopy (FE-SEM) images. The processing scheme adopted in the proposed system focused on two steps. The¯rst step was to enhance cervical cell FE-SEM images in order to show the precancerous characterization indicator. A problem arises from the question of how to extract features which characterize cervical precancerous cells. For the¯rst step, a preprocessing technique called intensity transformation and morphological operation (ITMO) algorithm used to enhance the quality of images was proposed. The algorithm consisted of contrast stretching and morphological opening operations. The second step was to characterize the cervical cells to three classes, namely normal, low grade intra-epithelial squamous lesion (LSIL), and high grade intra-epithelial squamous lesion (HSIL). To di®eren-tiate between normal and precancerous cells of the cervical cell FE-SEM images, human papillomavirus (HPV) contained in the surface of cells were used as indicators. In this paper, we investigated the use of texture as a tool in determining precancerous cell images based on the observation that cell images have a distinct visual texture. Gray level co-occurrences matrix (GLCM) technique was used to extract the texture features. To con¯rm the system's performance, the system was tested using 150 cervical cell FE-SEM images. The results showed that the accuracy, sensitivity and speci¯city of the proposed system are 95.7%, 95.7% and 95.8%, respectively.

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Intelligent Screening Systems for Cervical Cancer

Cited by 58 publications

References 116 publications

Multi-feature based benchmark for cervical dysplasia classification evaluation

Multi-feature based benchmark for cervical dysplasia classification evaluation

Segmentation of Cervical Cells for Automated Screening of Cervical Cancer: A Review

A system for detection of cervical precancerous in field emission scanning electron microscope images using texture features

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