Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm

William, Wasswa; Ware, Andrew; Basaza-Ejiri, Annabella Habinka; Obungoloch, Johnes

doi:10.1016/j.imu.2019.02.001

Cited by 60 publications

(38 citation statements)

References 44 publications

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“…In this section, the above three parameters were used to evaluate the performance of the proposed approach for image extraction. To verify the performance of the optimal threshold, computational time, and unclassified rate evaluation for the characteristic parameters of a coal dust image, several approaches that are widely used to process particulate images were compared by using images of different particle sizes: multiscale image acquisition (MSIA) [26], Daubechies wavelet transform (DWT) [27], Frenkel-Halsey-Hill (FHH) [28], grey-level cooccurrence matrix (GLCM) [29], fuzzy C-means (FCM) [9], Gabor filter [30], FPM, and SFC. In the process of testing the simulations with the approaches in the above works, the adopted test conditions, such as temperature, humidity, light intensity, coal dust sample specifications and other parameters, are not the same, so the measured indexes are different.…”

Section: B Discussion 1) Parameters Analysismentioning

confidence: 99%

“…However, this method ignored the spatial information between image targets with little grey value differences and the conditions of overlapping grey value areas. The histograms did not have obvious double peaks; thus, the extraction effect was poor [9]. The above research method considered the distribution of imagery greyscale space more effectively than other methods, greatly improving the accuracy of image extraction.…”

Section: Introductionmentioning

confidence: 99%

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Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

Wang

Zheng²,

Pan

et al. 2020

IEEE Access

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High-precision extraction of particulate characteristic modes is essential for dust explosion safety measurements, such as particulate concentration and size distribution. A new solution based on information entropy multi-decision attribute reduction fuzzy rough set is proposed to analyse the particulate morphology characteristics, which effectively avoids the shortcomings of traditional technology (low accuracy, stochasticity, etc.). The proposed approach consists of three stages: membership function modelling, attribute reduction, and maximum information entropy threshold segmentation. The membership coefficient was determined with a multi-segment function by developing the fuzzy degree of the membership model for dust image pixels. The fuzzy dependence of the conditional attribute was determined to extract the fuzzy attribute reduction. Finally, the model of coal dust particulates with information entropy was improved to extract the maximum segmentation threshold, which is significant for classification. The proposed methods were evaluated over a sequence of 30 image sets. The unclassified rate evaluation reached 0.978 for particle sizes ≥ 200 µm, 0.958 for [75 µm, 200 µm] and 0.950 for particle sizes < 75 µm. The proposed reduction approach offered a performance improvement in terms of more important attribute implementation. The paper demonstrated that the maximum information entropy reduction model can remove the redundant attributes without compromising the precision.

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Section: B Discussion 1) Parameters Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

Wang

Zheng²,

Pan

et al. 2020

IEEE Access

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“…Previous studies show that feature extraction and selecting non-redundant features is an important part of the classification process and it affects the classification result significantly. Different methods have been explored over the years, like traditionally handcrafted feature extraction and feature selection [2], simulated annealing (SA) [34], convolutional neural networks [38], fuzzy-C means [35], to name a few. Some have given very good results in binary classification but not so much in multi-class classification, only a few have successfully given good results even for multi-class classification problem [6,8,24].…”

Section: Related Workmentioning

confidence: 99%

Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

Basak

Kundu

Chakraborty³

et al. 2021

SN COMPUT. SCI.

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Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes deep learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts deep feature from several convolution neural network (CNN) models and uses a two-step feature reduction approach to ensure reduction in computation cost and faster convergence. The features extracted from the CNN models form a large feature space whose dimensionality is reduced using principal component analysis while preserving 99% of the variance. A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the grey wolf optimizer, thus improving the classification performance. Finally, the selected feature subset is used to train an support vector machine classifier for generating the final predictions. The proposed framework is evaluated on three publicly available benchmark datasets: Mendeley Liquid Based Cytology (4-class) dataset, Herlev Pap Smear (7-class) dataset, and the SIPaKMeD Pap Smear (5-class) dataset achieving classification accuracies of 99.47, 98.32 and 97.87%, respectively, thus justifying the reliability of the approach. The relevant codes for the proposed approach can be found in: https:// github. com/ DVLP-CMATE RJU/ Two-Step-Featu re-Enhan cement.

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“…In most literature, the classification of Pap smear images consists of a binary separation between normal and abnormal cell (two classes), using different methodologies such as Support Vector Machines (SVM) ( Chen et al, 2014 ; Chankong, Theera-Umpon & Auephanwiriyakul, 2014 ; Kashyap et al, 2016 ; Bora et al, 2017 ), k -Nearest Neighbours (kNN) ( Chankong, Theera-Umpon & Auephanwiriyakul, 2014 ; Bora et al, 2017 ; Marinakis, Dounias & Jantzen, 2009 ; Fekri Ershad, 2019 ), Fuzzy c -Means Algorithm (FCM) ( Chankong, Theera-Umpon & Auephanwiriyakul, 2014 ; William et al, 2019 ), k -Means clustering ( Paul, Bhowmik & Bhattacharjee, 2015 ), Artificial Neural Networks (ANN) ( Chankong, Theera-Umpon & Auephanwiriyakul, 2014 ), and, more recently, Convolutional Neural Networks (CNN) ( Zhang et al, 2017 ; Lin et al, 2019 ; Kurnianingsih et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

Ordinal losses for classification of cervical cancer risk

Albuquerque

Cruz

Cardoso

2021

PeerJ Computer Science

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Cervical cancer is the fourth leading cause of cancer-related deaths in women, especially in low to middle-income countries. Despite the outburst of recent scientific advances, there is no totally effective treatment, especially when diagnosed in an advanced stage. Screening tests, such as cytology or colposcopy, have been responsible for a substantial decrease in cervical cancer deaths. Cervical cancer automatic screening via Pap smear is a highly valuable cell imaging-based detection tool, where cells must be classified as being within one of a multitude of ordinal classes, ranging from abnormal to normal. Current approaches to ordinal inference for neural networks are found to not sufficiently take advantage of the ordinal problem or to be too uncompromising. A non-parametric ordinal loss for neuronal networks is proposed that promotes the output probabilities to follow a unimodal distribution. This is done by imposing a set of different constraints over all pairs of consecutive labels which allows for a more flexible decision boundary relative to approaches from the literature. Our proposed loss is contrasted against other methods from the literature by using a plethora of deep architectures. A first conclusion is the benefit of using non-parametric ordinal losses against parametric losses in cervical cancer risk prediction. Additionally, the proposed loss is found to be the top-performer in several cases. The best performing model scores an accuracy of 75.6% for seven classes and 81.3% for four classes.

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Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm

Cited by 60 publications

References 44 publications

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

Ordinal losses for classification of cervical cancer risk

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