Random Forest Based Deep Hybrid Architecture for Histopathological Breast Cancer Images Classification

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

doi:10.1007/978-3-031-10450-3_1

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Intensity normalization (Kociołek et al , 2020) and Contrast Limited Adaptive Histogram Equalization (Yussof et al , 2013) were employed for preprocessing the BreakHis dataset. Similar procedures were used in Zerouaoui et al (2021) and Nakach et al (2022b), with the objective of removing noise, eliminating shadows and improving the contrast of images. As shown in Table I, almost 67 per cent of the histological images in the BreakHis dataset were malignant.…”

Section: Methodsmentioning

confidence: 99%

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Nakach

Zerouaoui²,

Idri³

2023

DTA

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PurposeHistopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.Design/methodology/approachThree pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.FindingsThis study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.Originality/valueThe best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

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Section: Methodsmentioning

confidence: 99%

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Nakach

Zerouaoui²,

Idri³

2023

DTA

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DHHoE: Deep hybrid homogenous ensemble for digital histological breast cancer classification

2023

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The progress of deep learning architectures, machine learning models and pathology slide digitization is an encouraging step toward meeting the growing demand for more precise classification and prediction diagnosis for the breast tumours. The BreakHis dataset with four magnification factors (40X, 100X, 200X and 400X), as well as seven deep learning architectures used for feature extraction (DenseNet 201, Inception ResNet V2, Inception V3, ResNet 50, MobileNet V2,VGG16 and VGG19), four machine learning models for classification (MLP, SVM, DT, and KNN), and two combination rules (hard and weighted voting) were investigated in this paper to design and evaluate a new proposed approach consisting of building deep hybrid homogenous ensemble. Additionally, the best proposed models were compared to deep stacked, deep bagging, deep boosting, and deep hybrid heterogenous ensemble to choose the best strategy in building deep ensemble learning techniques. The four performance measures accuracy, precision, recall, and F1‐score were used in the empirical evaluations, as well as 5‐fold cross‐validation, the Scott Knott statistical test, and the Borda Count voting method. The results demonstrated the new approach's potential since it outscored both singles and other deep ensemble learning strategies, achieving accuracy values of 98.3% and 97.7% for the MFs 40X, 100X and 200X, 400X, respectively. The empirical results demonstrated that the proposed ensembles are impactful for histopathological breast cancer images classification, and they provided a promising tool to assist pathologists in the diagnosis of breast cancer.

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Random Forest Based Deep Hybrid Architecture for Histopathological Breast Cancer Images Classification

Cited by 2 publications

References 34 publications

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

DHHoE: Deep hybrid homogenous ensemble for digital histological breast cancer classification

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