scholarly journals A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ankur Manna ◽  
Rohit Kundu ◽  
Dmitrii Kaplun ◽  
Aleksandr Sinitca ◽  
Ram Sarkar
Keyword(s):  
Pap Smear ◽  
Linear Functions ◽  
Classification Model ◽  
Ensemble Technique ◽  
Fast Detection ◽  
Proposed Model ◽  
Slide Image ◽  
Benchmark Datasets ◽  
Cervical Cells ◽  
Fusion Of Classifiers

AbstractCervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime importance for eradicating the disease from the patient’s body. However, regular population-wise screening of cancer is limited by its expensive and labour intensive detection process, where clinicians need to classify individual cells from a stained slide consisting of more than 100,000 cervical cells, for malignancy detection. Thus, Computer-Aided Diagnosis (CAD) systems are used as a viable alternative for easy and fast detection of cancer. In this paper, we develop such a method where we form an ensemble-based classification model using three Convolutional Neural Network (CNN) architectures, namely Inception v3, Xception and DenseNet-169 pre-trained on ImageNet dataset for Pap stained single cell and whole-slide image classification. The proposed ensemble scheme uses a fuzzy rank-based fusion of classifiers by considering two non-linear functions on the decision scores generated by said base learners. Unlike the simple fusion schemes that exist in the literature, the proposed ensemble technique makes the final predictions on the test samples by taking into consideration the confidence in the predictions of the base classifiers. The proposed model has been evaluated on two publicly available benchmark datasets, namely, the SIPaKMeD Pap Smear dataset and the Mendeley Liquid Based Cytology (LBC) dataset, using a 5-fold cross-validation scheme. On the SIPaKMeD Pap Smear dataset, the proposed framework achieves a classification accuracy of 98.55% and sensitivity of 98.52% in its 2-class setting, and 95.43% accuracy and 98.52% sensitivity in its 5-class setting. On the Mendeley LBC dataset, the accuracy achieved is 99.23% and sensitivity of 99.23%. The results obtained outperform many of the state-of-the-art models, thereby justifying the effectiveness of the same. The relevant codes of this proposed model are publicly available on GitHub.

scholarly journals Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

2021 ◽  
Vol 11 (24) ◽  
pp. 11845
Author(s):  
Ansar Siddique ◽  
Asiya Jan ◽  
Fiaz Majeed ◽  
Adel Ibrahim Qahmash ◽  
Noorulhasan Naveed Quadri ◽  
...  
Keyword(s):  
Academic Performance ◽  
Early Stage ◽  
Educational Data Mining ◽  
Secondary Level ◽  
Classification Model ◽  
Student Academic Performance ◽  
Proposed Model ◽  
Ensemble Algorithms ◽  
Fusion Of Classifiers

In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.

A gating context-aware text classification model with BERT and graph convolutional networks

2021 ◽  
pp. 1-13
Author(s):  
Weiqi Gao ◽  
Hao Huang
Keyword(s):  
Text Classification ◽  
Structural Data ◽  
Classification Model ◽  
Context Aware ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Gating Mechanism ◽  
Document Frequency ◽  
Proposed Model ◽  
Benchmark Datasets

Graph convolutional networks (GCNs), which are capable of effectively processing graph-structural data, have been successfully applied in text classification task. Existing studies on GCN based text classification model largely concerns with the utilization of word co-occurrence and Term Frequency-Inverse Document Frequency (TF–IDF) information for graph construction, which to some extent ignore the context information of the texts. To solve this problem, we propose a gating context-aware text classification model with Bidirectional Encoder Representations from Transformers (BERT) and graph convolutional network, named as Gating Context GCN (GC-GCN). More specifically, we integrates the graph embedding with BERT embedding by using a GCN with gating mechanism enables the acquisition of context coding. We carry out text classification experiments to show the effectiveness of the proposed model. Experimental results shown our model has respectively obtained 0.19%, 0.57%, 1.05% and 1.17% improvements over the Text-GCN baseline on the 20NG, R8, R52, and Ohsumed benchmark datasets. Furthermore, to overcome the problem that word co-occurrence and TF–IDF are not suitable for graph construction for short texts, Euclidean distance is used to combine with word co-occurrence and TF–IDF information. We obtain an improvement by 1.38% on the MR dataset compared to Text-GCN baseline.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

BiLabel-Specific Features for Multi-Label Classification

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Min-Ling Zhang ◽  
Jun-Peng Fang ◽  
Yi-Bo Wang
Keyword(s):  
Predictive Models ◽  
Comparative Studies ◽  
State Of The Art ◽  
Classification Model ◽  
Generation Process ◽  
Prototype Selection ◽  
Class Label ◽  
Benchmark Datasets ◽  
Label Correlations ◽  
Class Labels

In multi-label classification, the task is to induce predictive models which can assign a set of relevant labels for the unseen instance. The strategy of label-specific features has been widely employed in learning from multi-label examples, where the classification model for predicting the relevancy of each class label is induced based on its tailored features rather than the original features. Existing approaches work by generating a group of tailored features for each class label independently, where label correlations are not fully considered in the label-specific features generation process. In this article, we extend existing strategy by proposing a simple yet effective approach based on BiLabel-specific features. Specifically, a group of tailored features is generated for a pair of class labels with heuristic prototype selection and embedding. Thereafter, predictions of classifiers induced by BiLabel-specific features are ensembled to determine the relevancy of each class label for unseen instance. To thoroughly evaluate the BiLabel-specific features strategy, extensive experiments are conducted over a total of 35 benchmark datasets. Comparative studies against state-of-the-art label-specific features techniques clearly validate the superiority of utilizing BiLabel-specific features to yield stronger generalization performance for multi-label classification.

scholarly journals A contour property based approach to segment nuclei in cervical cytology images

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Iram Tazim Hoque ◽  
Nabil Ibtehaz ◽  
Saumitra Chakravarty ◽  
M. Saifur Rahman ◽  
M. Sohel Rahman
Keyword(s):  
Pap Smear ◽  
State Of The Art ◽  
Cervical Cytology ◽  
Average Intensity ◽  
Nucleus Size ◽  
Real Dataset ◽  
Art Methods ◽  
Convolution Filter ◽  
Cervical Cells ◽  
Nucleus Segmentation

Abstract Background Segmentation of nuclei in cervical cytology pap smear images is a crucial stage in automated cervical cancer screening. The task itself is challenging due to the presence of cervical cells with spurious edges, overlapping cells, neutrophils, and artifacts. Methods After the initial preprocessing steps of adaptive thresholding, in our approach, the image passes through a convolution filter to filter out some noise. Then, contours from the resultant image are filtered by their distinctive contour properties followed by a nucleus size recovery procedure based on contour average intensity value. Results We evaluate our method on a public (benchmark) dataset collected from ISBI and also a private real dataset. The results show that our algorithm outperforms other state-of-the-art methods in nucleus segmentation on the ISBI dataset with a precision of 0.978 and recall of 0.933. A promising precision of 0.770 and a formidable recall of 0.886 on the private real dataset indicate that our algorithm can effectively detect and segment nuclei on real cervical cytology images. Tuning various parameters, the precision could be increased to as high as 0.949 with an acceptable decrease of recall to 0.759. Our method also managed an Aggregated Jaccard Index of 0.681 outperforming other state-of-the-art methods on the real dataset. Conclusion We have proposed a contour property-based approach for segmentation of nuclei. Our algorithm has several tunable parameters and is flexible enough to adapt to real practical scenarios and requirements.

scholarly journals Profiles of Human Papillomavirus Detection of the Multinucleated Cells in Cervical Smears

2021 ◽  
Vol 9 (8) ◽  
pp. 1575
Author(s):  
Kaori Okayama ◽  
Toshiyuki Sasagawa ◽  
Koji Teruya ◽  
Mizue Oda ◽  
Masahiko Fujii ◽  
...  
Keyword(s):  
Pap Smear ◽  
Morphological Changes ◽  
Human Papillomaviruses ◽  
Multinucleated Cells ◽  
Hpv Genotypes ◽  
Atypical Cells ◽  
Intraepithelial Lesion ◽  
Liquid Based Cytology ◽  
Cervical Cells ◽  
High Risk Hpv

Many genotypes of human papillomaviruses (HPVs) may lead to morphological changes in cells, resulting in various atypical cells, such as multinucleated cells (MNCs) and koilocytes, in the cervix. However, the relationships between the profiles of HPV genotypes and MNCs are not exactly known. Thus, this study comprehensively profiles the HPV genotypes in MNCs using a microdissection method. HPV genotypes and MNCs were detected in 651 cases with an abnormal Pap smear by liquid-based cytology. Specific HPV genotypes were also detected, including HPV16, 34, and 56, which might be associated with MNCs. This result suggests that the high-risk HPV genotypes, such as HPV16 and 56, are associated with the atypical changes in MNC morphology from normal cervical cells. The results also show that MNCs may be a predictor of squamous intraepithelial lesion.

scholarly journals A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

2021 ◽  
Vol 11 (9) ◽  
pp. 4091
Author(s):  
Débora N. Diniz ◽  
Mariana T. Rezende ◽  
Andrea G. C. Bianchi ◽  
Claudia M. Carneiro ◽  
Daniela M. Ushizima ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Pap Smear ◽  
Hierarchical Classification ◽  
Cell Nuclei ◽  
Test Analysis ◽  
Pap Smear Test ◽  
Microscopy Image ◽  
Feature Based ◽  
Cervical Cells ◽  
Number Of Classes

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.

scholarly journals Sales Growth Rate Forecasting Using Improved PSO and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xibin Wang ◽  
Junhao Wen ◽  
Shafiq Alam ◽  
Xiang Gao ◽  
Zhuo Jiang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Decisive Role ◽  
Convergence Speed ◽  
Classification Model ◽  
Support Vector ◽  
Local Optimum ◽  
Sales Growth ◽  
Local Optima ◽  
Proposed Model ◽  
Forecasting Performance

Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO) for sales growth rate forecasting. We use support vector machine (SVM) as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

scholarly journals JKRW Link Prediction – A New Ensemble Technique Based on Merging Other Known Techniques in The Social Network Analysis

2021 ◽  
Vol 15 (12) ◽  
pp. 125
Author(s):  
Aya Taleb ◽  
Rizik M. H. Al-Sayyed ◽  
Hamed S. Al-Bdour
Keyword(s):  
Link Prediction ◽  
Preferential Attachment ◽  
Large Data ◽  
The Other ◽  
Jaccard Coefficient ◽  
Ensemble Technique ◽  
Signed Rank ◽  
Proposed Model ◽  
Different Populations ◽  
A New Technique

In this research, a new technique to improve the accuracy of the link prediction for most of the networks is proposed; it is based on the prediction ensemble approach using the voting merging technique. The new proposed ensemble called Jaccard, Katz, and Random models Wrapper (JKRW), it scales up the prediction accuracy and provides better predictions for different sizes of populations including small, medium, and large data. The proposed model has been tested and evaluated based on the area under curve (AUC) and accuracy (ACC) measures. These measures applied to the other models used in this study that has been built based on the Jaccard Coefficient, Katz, Adamic/Adar, and Preferential attachment. Results from applying the evaluation matrices verify the improvement of JKRW effectiveness and stability in comparison to the other tested models.  The results from applying the Wilcoxon signed-rank method (one of the non-parametric paired tests) indicate that JKRW has significant differences compared to the other models in the different populations at <strong>0.95</strong> confident interval.

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