Development of novel ensemble model using stacking learning and evolutionary computation techniques for automated hepatocellular carcinoma detection

Książek, Wojciech; Hammad, Mohamed; Pławiak, Paweł; Acharya, U. Rajendra; Tadeusiewicz, Ryszard

doi:10.1016/j.bbe.2020.08.007

Cited by 33 publications

(42 citation statements)

References 40 publications

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“…The ensemble learning approach is employed using seven machine learning algorithms that are stacked together for automated detection for hepatocellular carcinoma [ 40 ] and using collaborative representation classification with boosting technique for classifying the hyperspectral image [ 41 ]. The flow map of the proposed automated method for detecting early cervical cancer is shown in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Venkatesan

Sumithra

Karthick

et al. 2021

BioMed Research International

121

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Traditional screening of cervical cancer type classification majorly depends on the pathologist’s experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.

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Section: Methodsmentioning

confidence: 99%

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Venkatesan

Sumithra

Karthick

et al. 2021

BioMed Research International

121

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“…Then, three classifiers: KNN, RF, and XGB, with excellent performance, are employed to verify the suggested approach. While Ksiazek et al [4] developed a stacking ensemble learning technique with genetic optimization methodology to select each classification model's features to achieve excellent performance for detecting HCC after utilizing the HCC dataset, in this case, they gained an accuracy: 90.30% and an F1-score: 88.57%, respectively, to detect HCC [4]. On the other hand, Sharma and Kumar [64] presented an ensemble learning model to predict HCC survival.…”

Section: Ensemble Learning Techniquesmentioning

confidence: 99%

“…As living standards and lifestyles are continuously developing, it is becoming overwhelmingly common [3]. While in the case of HCC, it is the fourth most regular reason for cancer-related mortality in the world [4]. It happens mainly in chronic liver ailments, such as cirrhosis induced by hepatitis C or hepatitis B [5].…”

Section: Introductionmentioning

confidence: 99%

“…While in the case of HCC disease, it is recognized with computed tomography (CT) scan and magnetic resonance imaging [5]. However, this type of liver cancer is not early detected because of the lack of indications in affected sufferers [4]. In this regard, various techniques have been adopted, either manually by physicians or by anything else, but such procedures have notable shortcomings in predicting such diseases in their beginning phases to diagnose them.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, several ML-based methodologies have been suggested and published for predicting such diseases, and we can categorize these methodologies based on the limitations mentioned. In the case of prediction of the HCC disease, the authors either employed class rebalancing algorithms: SMOTE and cluster-based oversampling technique included in [10], [26], [27] to solve the class balancing problems, or utilizes feature selections techniques: Linear Discriminant Analysis (LDA) and Neighborhood Component Analysis (NCA) and many more included in [28], [30], [32], [33], [34], [36] for the selection of informative features, or employing robust single classifiers: decision tree (DT), support vector machine (SVM), and random forest (RF) included in [5] or using methods based on ensemble models and deep learning included in [53], [4], [63], [64] to enhancement the performance of their generated models after employing HCC dataset [10]. While in case of predicting diabetes disease, several previous methodologies either employed single classification models: RF, logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN) included [9], [13], or utilized deep learning-enabled feature selection techniques: multilayer perceptron (MLP) with adaptive particle swarm optimization with grey wolf optimization (APGWO) and GWO techniques included in [45] to select the optimum features, or utilized deep learning techniques: artificial neural network (ANN) included in [46] to classify the diabetes disease and enhancement the performance of their developed models after employing early-stage diabetes risk prediction dataset [9].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Dhar¹,

Ayele

2021

IEEE Access

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Various well-known health diseases affect millions of people worldwide. Sometimes in the early stage, the clinicians may not recognize several clinical symptoms due to lack of their symptoms reflection or anything else. Thus, such diseases are not easier to identify. There may have chances to grow these illnesses and affected millions of people worldwide. The risk factor of such diseases severity can be lessened, notably whenever an accurate early prediction is possible. This study presents an innovative multi-tier weighted ensemble learning model (MTWEL) for predicting several diseases such as diabetes and hepatocellular carcinoma (HCC) and, therefore, reduces such above-said problems from the sufferers and lessens the chances of mortality. In the MTWEL model, we have utilized two lists of base classifiers in which six various machine learning (ML) classifiers are assigned in each list to develop two weighted ensemble learning (EL) models and combine them to form the proposed model by employing a weighted voting approach. In the MTWEL model, the parameters of all employed classifiers are tuned through the genetic algorithm-enabled hyperparameter optimization technique to form the optimized base models. The weight of each chosen optimized base model and generated EL model(s) is calculated using Matthews correlation coefficient value with the optimized weight value. In this study, neighborhood component analysis is employed to reduce the dimension of the given input dataset. The suggested model's experimental outcomes are conducted on two real-world datasets to exhibit its performance. The suggested approach receives the best result in AUC values: 1.0 and 1.0, F1-score values: 0.9957 and 0.9947, and accuracy values: 0.9952 and 0.9929. Such outcomes in the form of performance exhibit that the proposed model is the best-suited model to predict several diseases than other techniques, and hence it helps clinicians make accurate decisions.

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NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Książek

Turza

Pławiak

2022

Numer Methods Biomed Eng

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Hepatocellular carcinoma (HCC) is one of the major challenges facing biomedical research. Despite the high lethality, methods to predict mortality for this type of aggressive malignant tumor are insufficient. Machine learning is recognized by many authors as a valuable, yet poorly studied tool in this field. Undoubtedly, searching for new feature selection methods is significant in building an effective machine-learning model. In this study, we propose the novel hybrid model using neighborhood components analysis, genetic algorithm and support vector machine classifier (NCA-GA-SVM). Because SVM works with default parameters characterized by low classification results, we decided to use GA for the proper optimization and feature selection. As reported in the available literature, NCA and GA obtain high classification results. Here, we decided to combine these approaches, building a two-level algorithm for HCC fatality prognosis. We used a well-known dataset collected from 165 patients at Coimbra's Hospital and University Center, Portugal. Our results revealed 96.36% classification accuracy and 95.52% F1-score. Additionally, we compared all data for these metrics published so far. We demonstrated that our algorithm achieved the highest accuracy and can be successfully applied for the assessment of hepatocellular carcinoma mortality in the future. Our findings bring methodological value for future HCC studies and emphasize the possibility of using machine-learning techniques to improve the quality of medical decisions.

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Development of novel ensemble model using stacking learning and evolutionary computation techniques for automated hepatocellular carcinoma detection

Cited by 33 publications

References 40 publications

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

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