Deep Hybrid AdaBoost Ensembles for Histopathological Breast Cancer Classification

Nakach, Fatima-Zahrae; Zerouaoui, Hasnae; Idri, Ali

doi:10.1007/978-3-031-04826-5_45

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…The existing works on classification are using the datasets with fewer number of sample images [1, 3, 5, 6, 17, 18, 21, 24, 28-30, 33, 34, 36, 41, 42, 47] that may not sufficient to train deep learning algorithms because training process of a deep learning network require a large amount of image data. The most frequently used datasets are the BreakHis [2,4,6,9,12,14,23,27,31,32,40,48] and the WBCD [1,5,10,17,21,34,41,42]. However, the WBCD dataset consists of only 569 or 699 instances with 32 features, while, the BreakHis dataset consists of 7909 images.…”

Section: Related Workmentioning

confidence: 99%

“…SELF ❼ The researchers have adopted machine learning [1,6,10,24,29] or deep learning [2,4,12,14,18,28,31,40,47,48] or ensemble learning [3, 5, 17, 20, 21, 23, 27, 30, 32-34, 36, 41, 42] techniques to address the breast cancer classification problem, and put their best efforts to improve the performance of their proposed approach(s). From Table 1, we observe that most of the existing works have mainly borrowed the deep learning techniques to address the breast cancer classification problem because of image datasets [2,4,9,12,14,18,20,23,28,31,32,40,47,47,48]. The Convolutional Neural Network (CNN), a deep learning model, performs well on image datasets because it handles the entire feature engineering phase and extracts features from an image in an efficient way.…”

Section: Related Workmentioning

confidence: 99%

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SELF: a stacked-based ensemble learning framework for breast cancer classification

Jakhar

Singh

2023

Evol. Intel.

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Nowadays, breast cancer is the most prevalent and jeopardous disease in women after lung cancer. During the past few decades, a substantial amount of cancer cases have been reported throughout the world. Among them, breast cancer has been a widely acknowledged category of cancer disease in women due to the lack of awareness. According to the world cancer survey report 2020, there are about 2.3 million cases and 685000 deaths have been reported all over the world. As, the patient-doctor ratio (PDR) is very high; consequently, there is an utmost need for a machinebased smart breast cancer diagnosis system that can detect cancer at its early stage and be cured more efficiently. The plan is to assemble scientists in both the restorative and the machine learning fields to progress toward this clinical application. This paper presents SELF, a stackedbased ensemble learning framework, to classify breast cancer at an early stage from the histopathological images of tumor cells with computeraided diagnosis tools. In this work, we use the BreakHis dataset with 7909 histopathological images obtained on 82 patients and Wisconsin Breast SELF Cancer Database (WBCD) with 569 instances for the performance evaluation. We have trained several distinct classifiers on both datasets and selected the best five common classifiers on the basis of their accuracy measure to create our ensemble model. We use the stacking ensemble technique and consider the Extra tree, Random Forest, Adaboost, Gradient Boosting, and KNN9 classifiers as the base learners, and logistic regression model as a final estimator. We have evaluated the performance of SELF on the BreakHis dataset and WBCD datasets, and achieved the testing accuracy of approximately 95% and 99% respectively. The result of the other performance parameters on BreakHis dataset also showed that our proposed framework outperforms with F1-Score, ROC, and MCC scores with values of 94.17%, 89.41%, and 80.81% respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SELF: a stacked-based ensemble learning framework for breast cancer classification

Jakhar

Singh

2023

Evol. Intel.

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“…Despite the encouraging classification results provided by ML and DL models, the use of one classifier does not always provide the best outperforming model and the higher accuracy in all circumstances since (1) it highly depends on the type of the problem dealt with and (2) each single classifier has its advantages and weaknesses (Hosni et al, 2019). To deal with this limitation, researchers investigated the ensemble learning approach (Ahmed and El Sadig, 2019;Idri et al, 2020;Kassani et al, 2020;Nakach et al, 2022;El Ouassif et al, 2021) which consists of combining single learners that are accurate and diverse in order to consolidate their advantages and overcome their weaknesses using a combination rule such as simple majority voting or weighted voting (Kuncheva, 2003). In the medical field and more precisely for BC diagnosis classification, many studies proposed the use of ensemble learning to improve the classification performances.…”

Section: Introductionmentioning

confidence: 99%

A new approach for histological classification of breast cancer using deep hybrid heterogenous ensemble

Zerouaoui

Idri

Alaoui³

2022

DTA

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PurposeHundreds of thousands of deaths each year in the world are caused by breast cancer (BC). An early-stage diagnosis of this disease can positively reduce the morbidity and mortality rate by helping to select the most appropriate treatment options, especially by using histological BC images for the diagnosis.Design/methodology/approachThe present study proposes and evaluates a novel approach which consists of 24 deep hybrid heterogenous ensembles that combine the strength of seven deep learning techniques (DenseNet 201, Inception V3, VGG16, VGG19, Inception-ResNet-V3, MobileNet V2 and ResNet 50) for feature extraction and four well-known classifiers (multi-layer perceptron, support vector machines, K-nearest neighbors and decision tree) by means of hard and weighted voting combination methods for histological classification of BC medical image. Furthermore, the best deep hybrid heterogenous ensembles were compared to the deep stacked ensembles to determine the best strategy to design the deep ensemble methods. The empirical evaluations used four classification performance criteria (accuracy, sensitivity, precision and F1-score), fivefold cross-validation, Scott–Knott (SK) statistical test and Borda count voting method. All empirical evaluations were assessed using four performance measures, including accuracy, precision, recall and F1-score, and were over the histological BreakHis public dataset with four magnification factors (40×, 100×, 200× and 400×). SK statistical test and Borda count were also used to cluster the designed techniques and rank the techniques belonging to the best SK cluster, respectively.FindingsResults showed that the deep hybrid heterogenous ensembles outperformed both their singles and the deep stacked ensembles and reached the accuracy values of 96.3, 95.6, 96.3 and 94 per cent across the four magnification factors 40×, 100×, 200× and 400×, respectively.Originality/valueThe proposed deep hybrid heterogenous ensembles can be applied for the BC diagnosis to assist pathologists in reducing the missed diagnoses and proposing adequate treatments for the patients.

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SELF: A Stacked-based Ensemble Learning Framework for Breast Cancer Classification

Jakhar

Singh

2022

Preprint

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Nowadays, breast cancer is the most prevalent and jeopardous disease in women after lung cancer. During the past few decades, a substantial amount of cancer cases have been reported throughout the world. Among them, breast cancer has been a widely acknowledged category of cancer disease in women due to the lack of awareness. According to the world cancer survey report 2020, about 2.3 million cases and 685000 deaths have been reported worldwide. As, the patient-doctor ratio (PDR) is very high; consequently, there is an utmost need for a machine-based smart breast cancer diagnosis system that can detect cancer at its early stage and be cured more efficiently. The plan is to assemble scientists in both the restorative and the machine learning fields to progress toward this clinical application. This paper presents SELF, a stacked-based ensemble learning framework, to classify breast cancer at an early stage from the histopathological images of tumor cells with computer-aided diagnosis tools. In this work, we use the BreakHis dataset with 7909 histopathological images obtained on 82 patients and Wisconsin Breast Cancer Database (WBCD) with 569 instances for the performance evaluation. We have trained several distinct classifiers on both datasets and SELF selected the best five common classifiers on the basis of their accuracy measure to create our ensemble model. We use the stacking ensemble technique and consider the Extra tree, Random Forest, Adaboost, Gradient Boosting, and KNN9 classifiers as the base learners, and the logistic regression model as a final estimator. We have evaluated the performance of SELF on the BreakHis dataset and WBCD datasets and achieved the testing accuracy of approximately 95% and 99% respectively. The result of the other performance parameters on the BreakHis dataset also showed that our proposed framework outperforms with F1-Score, ROC, and MCC scores with values of 94.17%, 89.41%, and 80.81% respectively.

show abstract

Deep Hybrid AdaBoost Ensembles for Histopathological Breast Cancer Classification

Cited by 4 publications

References 24 publications

SELF: a stacked-based ensemble learning framework for breast cancer classification

SELF: a stacked-based ensemble learning framework for breast cancer classification

A new approach for histological classification of breast cancer using deep hybrid heterogenous ensemble

SELF: A Stacked-based Ensemble Learning Framework for Breast Cancer Classification

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