@article {Nakach2022446,
	title = {Deep Hybrid AdaBoost Ensembles for Histopathological Breast Cancer Classification},
	journal = {Lecture Notes in Networks and Systems},
	volume = {468 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {446-455},
	abstract = {Breast cancer (BC) is the most common diagnosed cancer type and one of the top leading causes of death in women worldwide. The early diagnosis of this type of cancer is the main driver of high survival rate. This paper aims to use homogenous ensemble learning and transfer learning for binary classification of BC histological images over the four-magnification factor (MF) values of the BreakHis dataset: 40X, 100X, 200X, and 400X. The proposed ensembles are implemented using a hybrid architecture (HA) that combines: (1) three of the most recent deep learning (DL) techniques as feature extractors (FE): DenseNet_201, Inception_V3, and MobileNet_V2, and (2) the boosting method AdaBoost with Decision Tree (DT) as a base learner. The study evaluated and compared: the ensembles designed with the same HA but with different number of trees (50, 100, 150 and 200), the single DT classifiers with the best AdaBoost ensembles and the best AdaBoost ensembles of each FE over each MF. The empirical evaluations used: four classification performance criteria (accuracy, recall, precision and F1-score), 5-fold cross-validation, Scott Knott (SK) statistical test to select the best cluster of the outperforming models, and Borda Count voting system to rank the best performing ones. Results showed the potential of combining DL techniques for FE and AdaBoost boosting method to classify BC in malignant and benign tumors, furthermore the AdaBoost ensemble constructed using 200 trees, DenseNet_201 as FE and MF 200X achieved the best mean accuracy value with 90.36\%. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-04826-5_45},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130279063\&doi=10.1007\%2f978-3-031-04826-5_45\&partnerID=40\&md5=59d8d87203f77eed9f4da56cae519226},
	author = {Nakach, F.-Z. and Zerouaoui, H. and Idri, A.}
}
@article {Nakach20221043,
	title = {Hybrid deep boosting ensembles for histopathological breast cancer classification},
	journal = {Health and Technology},
	volume = {12},
	number = {6},
	year = {2022},
	note = {cited By 0},
	pages = {1043-1060},
	abstract = {Purpose: Breast cancer (BC) is the most common diagnosed cancer type and one of the top leading causes of death in women worldwide. This paper aims to investigate ensemble learning and transfer learning for binary classification of BC histological images over the four-magnification factor (MF) values of the BreakHis dataset: 40X, 100X, 200X, and 400X. Methods: The proposed homogeneous ensembles are implemented using a hybrid architecture that combines: (1) three of the most recent deep learning (DL) techniques for feature extraction: DenseNet_201, MobileNet_V2, and Inception_V3, and (2) four of the most popular boosting methods for classification: AdaBoost (ADB), Gradient Boosting Machine (GBM), LightGBM (LGBM) and XGBoost (XGB) with Decision Tree (DT) as a base learner. The study evaluated and compared: (1) a set of boosting ensembles designed with the same hybrid architecture and different number of trees (50, 100, 150 and 200); (2) different boosting methods, and (3) the single DT classifier with the best boosting ensembles. The empirical evaluations used: four classification performance criteria (accuracy, recall, precision and F1-score), the fivefold cross-validation, Scott Knott statistical test to select the best cluster of the outperforming models, and Borda Count voting system to rank the best performing ones. Results: The best boosting ensemble achieved an accuracy value of 92.52\% and it was constructed using XGB with 200 trees and Inception_V3 as feature extractor (FE). Conclusions: The results showed the potential of combining DL techniques for feature extraction and boosting ensembles to classify BC in malignant and benign tumors. {\textcopyright} 2022, The Author(s) under exclusive licence to International Union for Physical and Engineering Sciences in Medicine (IUPESM).},
	doi = {10.1007/s12553-022-00709-z},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141693839\&doi=10.1007\%2fs12553-022-00709-z\&partnerID=40\&md5=9e968402920eaed7fb6aaa79efc60f1a},
	author = {Nakach, F.-Z. and Zerouaoui, H. and Idri, A.}
}
@article {Nakach20223,
	title = {Random Forest Based Deep Hybrid Architecture for Histopathological Breast Cancer Images Classification},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {13376 LNCS},
	year = {2022},
	note = {cited By 0},
	pages = {3-18},
	abstract = {Breast cancer is the most common cancer in women worldwide. While the early diagnosis and treatment can significantly reduce the mortality rate, it is a challenging task for pathologists to accurately estimate the cancerous cells and tissues. Therefore, machine learning techniques are playing a significant role in assisting pathologists and improving the diagnosis results. This paper proposes a hybrid architecture that combines: three of the most recent deep learning techniques for feature extraction (DenseNet_201, Inception_V3, and MobileNet_V2) and random forest to classify breast cancer histological images over the BreakHis dataset with its four magnification factors: 40X, 100X, 200X and 400X. The study evaluated and compared: (1) the developed random forest models with their base learners, (2) the designed random forest models with the same architecture but with a different number of trees, (3) the decision tree classifiers with the best random forest models and (4) the best random forest models of each feature extractor. The empirical evaluations used: four classification performance criteria (accuracy, sensitivity, precision and F1-score), 5-fold cross-validation, Scott Knott statistical test, and Borda Count voting method. The best random forest model achieved an accuracy mean value of 85.88\%, and was constructed using 9 trees, 200X as a magnification factor, and Inception_V3 as a feature extractor. The experimental results demonstrated that combining random forest with deep learning models is effective for the automatic classification of malignant and benign tumors using histopathological images of breast cancer. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Breast Cancer, Classification (of information), Decision trees, Deep learning, Diagnosis, Diseases, early diagnosis, Feature extractor, hybrid, Hybrid architectures, Image classification, Images classification, Learning systems, Magnification factors, Medical imaging, Random forest modeling, Random forests, Transfer learning},
	doi = {10.1007/978-3-031-10450-3_1},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135002675\&doi=10.1007\%2f978-3-031-10450-3_1\&partnerID=40\&md5=892757314e050f81e9b0dba117f2d64a},
	author = {Nakach, F.-Z. and Zerouaoui, H. and Idri, A.}
}
