Clustering Techniques on Pap-smear Images for the Detection of Cervical Cancer

Arya, Mithlesh; Mittal, Namita; Singh, Girdhari

doi:10.15412/j.jbtw.01070106

Cited by 9 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed model yielded an average precision value of 0.424, with only 140 labeled images. Furthermore, traditional feature extraction practices such as histogram of oriented gradients (HOG) [172], a combination of K-means with fuzzy c-means clustering [173], and a fusion of textual and statistical information [174] are potent procedures in analyzing the pap cell. However, they fail to produce the state of the art accuracies.…”

Section: ) Potential Classification Methodsmentioning

confidence: 99%

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. Pap smear test is the most extensively performed screening method for early detection of cervical cancer. However, this hand-operated screening approach suffers from a high false-positive result because of human errors. To improve the accuracy and manual screening practice, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. In this survey, we provide a comprehensive study of the state of the art approaches based on deep learning for the analysis of cervical cytology images. Firstly, we introduce deep learning and its simplified architectures that have been used in this field. Secondly, we discuss the publicly available cervical cytopathology datasets and evaluation metrics for segmentation and classification tasks. Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. Finally, we investigate the existing methodology along with the most suitable techniques for the analysis of pap smear cells.

show abstract

Section: ) Potential Classification Methodsmentioning

confidence: 99%

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In addition, this work was further improved in [32], they proposed a dynamic sparse contour searching algorithm to locate the weak contour points of cytoplasm in overlapping regions, and the Gradient Vector Flow Snake model is finally employed to extract the accurate cell contour based on the located contour points. In order to eliminate the limitations of hand-crafted feature, [19,33,34] Different from recognition based on single-cell image, it is more sophisticated to segment nuclei and cytoplasm of images containing a large number of isolated cells and cell clusters, especially overlapping cells, and this has attracted increasing research interests [35,36,37,38,39]. William et al [40] trained a pixel level classifier on cell nuclei, cytoplasm, background and debris using a Trainable Weka Segmentation(TWS) toolkit [41] to identify and segment different objects on a slide.…”

Section: Cervical Cell Recognitionmentioning

confidence: 99%

A novel automation-assisted cervical cancer reading method based on convolutional neural network

Xiang

Sun

Pan

et al. 2020

Biocybernetics and Biomedical Engineering

View full text Add to dashboard Cite

Cervical cytology screening using Pap smear or liquid-based cytology is one of the most widely followed and accepted method. Automation-assisted screening based on cervical cytology has become a necessity due to the manual screening method operated by a visual analysis for cervical cell specimen under the microscope of the glass slide is usually labor-intensive and time-consuming. While automation-assisted reading system can improve efficiency, their performance often relies on the success of accurate cell segmentation and hand-craft feature extraction. This paper presents an efficient and totally segmentation-free method for automated cervical cell screening that utilizes modern object detector to directly detect cervical cells or clumps, without the design of specific hand-crafted feature. Specifically, we use the state-of-the-art CNN-based object detection methods, YOLOv3, as our baseline model. In order to improve the classification performance of hard examples which are four highly similar categories, we cascade an additional task-specific classifier. We also investigate the presence of unreliable annotations and coped with them by smoothing the distribution of noisy labels.We comprehensively evaluate our methods on our test set which is consisted of 1,014 annotated cervical cell images with size of 4000×3000 and complex cellular situation corresponding to 10 categories.Our model achieves 97.5% sensitivity (Sens) and 67.8% specificity (Spec) on cervical cell image-level screening. Moreover, we obtain a best mean Average Precision (mAP) of 63.4% on cervical cell-level diagnosis, and improve the Average Precision (AP) of hard examples which are the most valuable but most difficult to distinguish. Our automation-assisted cervical cell reading system not only achieves cervical cell image-level classification but also provides more detailed location and category reference information of abnormal cells. The results indicate feasible performance of our method, together with the efficiency and robustness, providing a new idea for future development of computer-assisted reading systems in clinical cervical screening.

show abstract

Pap smear classification using combination of global significant value, texture statistical features and time series features

Fekri-Ershad

2019

Multimed Tools Appl

View full text Add to dashboard Cite

Clustering Techniques on Pap-smear Images for the Detection of Cervical Cancer

Cited by 9 publications

References 0 publications

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

A novel automation-assisted cervical cancer reading method based on convolutional neural network

Pap smear classification using combination of global significant value, texture statistical features and time series features

Contact Info

Product

Resources

About