An improved nucleus segmentation for cervical cell images using FCM clustering and BPNN

Sharma, Bharti; Mangat, Kamaljeet Kaur

doi:10.1109/icacci.2016.7732332

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…A recall is the number of identified pixels from all the pixels in the ground truth. F1 score is the harmonic mean of precision and recall [90]. Mathematical expression for precision, recall, and F1 score is shown in and FN in all aspects.…”

Section: ) Evaluation Of Segmentation Methodsmentioning

confidence: 99%

“…The higher ZSI value symbolizes more reliable exactness. In [90], the author implements a backpropagation neural network for feature extraction and fuzzy c-means clustering to segment the nucleus. They obtain a ZSI value of 0.85.…”

Section: B Methods Analysismentioning

confidence: 99%

“…By using MSCN and graph partitioning, they got cytoplasm and nucleus segmentation accuracy of 90% and 85%, respectively. An automated CAD system is proposed in [90]. The author in this study improves the existing Fuzzy c-means clustering algorithm for nucleus segmentation by finding the optimum number of clusters instead of fixed clusters.…”

Section: A Reference Reviewmentioning

confidence: 99%

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A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

103

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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.

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Section: ) Evaluation Of Segmentation Methodsmentioning

confidence: 99%

Section: B Methods Analysismentioning

confidence: 99%

Section: A Reference Reviewmentioning

confidence: 99%

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A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

103

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“…The nuclei being present in the cervical cells are segmented by the double layered segmentation algorithm. The performance of the proposed segmentation algorithm is compared against the analogous segmentation approaches such as FCM+BPNN [4] and mean-shift based algorithm [6]. The experimental settings of the proposed algorithm are presented in table 1.…”

Section: Experimental Analysismentioning

confidence: 99%

“…A segmentation algorithm based on Fuzzy C Means (FCM) and Back Propagation Neural Networks (BPNN) is proposed in [4]. The shape features are extracted from the image and fed as input to the BPNN.…”

Section: Related Workmentioning

confidence: 99%

A Double Layered Segmentation Algorithm for Cervical Cell Images based on GHFCM and ABC

Lakshmi¹,

Ravi²

2017

IJIGSP

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Abstract-Cancer is a life threatening disease and it engulfs the lives of many women. Due to the technology advancement, the medical science is drastically improved. A statistical report claims that the diagnostic decisions of radiologists show more false positive rates, which is very dangerous. However, when the radiologists are supported by computer aided applications, the false positive results are considerably reduced. Understanding the potentiality of computer aided applications, this paper presents a double layered segmentation algorithm for cervical cell images. The entire work is subdivided into three important phases, which are cervical image preprocessing, coarse and fine level segmentation. The preprocessing phase attempts to remove the noise and enhance the image quality by means of adaptive mean filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) technique respectively. The coarse level segmentation process is achieved by Generalized Hierarchical Fuzzy C Means (GHFCM) and the fine level segmentation process is carried out by Artificial Bee Colony (ABC) algorithm. The performance of the proposed segmentation algorithm is analysed in terms of accuracy, sensitivity and specificity. The experimental results show the efficacy of the proposed segmentation algorithm.

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Early cervical cancer diagnosis using Sooty tern‐optimized CNN‐LSTM classifier

Chitra

Kumar

2022

Int J Imaging Syst Tech

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Detecting and categorizing Pap smear images automatically has been a difficult task over the past five decades. There is a significant need for the design and implementation of low‐cost, high‐efficiency screening systems. To address these issues, we have proposed a novel approach for cervical cancer classification. At first, each image is pre‐processed and cropped into 2043*1362 pixels. These images are separated into 681*681 pixels with six square patches. Image augmentation techniques such as image enhancement, image flipping, and image rotation are used to reduce the number of parameters necessary for subsequent processes. The cancer‐affected regions are correctly segmented with the help of kernel weighted fuzzy local information c‐means clustering (KWFLICM) model. The KWFLICM model mainly extracts the cytoplasm and nucleus present in the cell from the background region. The features of the cervical image are extracted using a convolution neural network (CNN), and these retrieved features, such as shape, color, and texture, are crucial for classification. Finally, the Sooty Tern Optimization (STO) algorithm with CNN‐based long short‐term memory classifier (CNN‐LSTM) identifies the four types of CC: normal, light dysplastic, severe dysplastic, and carcinoma. The images for the experiment were obtained from Herlev University Hospital in Denmark, and the software used for the implementation was MATLAB. According to the comparative analysis, the proposed CNN‐LSTM with STO algorithm demonstrates better results using various measures such as accuracy, specificity, sensitivity, and F‐score. Depending upon the experimental results, the proposed CNN‐LSTM with STO algorithm provides 99.80% accuracy, 99% specificity, 98.83% sensitivity, and 97.8 F‐score. The proposed methodology offers an improvement of 28.5% and 19.46% when compared with the random forest and ensemble classifier.

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An improved nucleus segmentation for cervical cell images using FCM clustering and BPNN

Cited by 12 publications

References 14 publications

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

A Double Layered Segmentation Algorithm for Cervical Cell Images based on GHFCM and ABC

Early cervical cancer diagnosis using Sooty tern‐optimized CNN‐LSTM classifier

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