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New design strategies of deep heterogenous convolutional neural networks ensembles for breast cancer diagnosis

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Abstract

One of the most consequential public health issues in the world and a major factor in women's mortality is breast cancer. Early detection and diagnosis can significantly improve the likelihood of survival. Therefore, this study suggests a deep end-to-end heterogeneous ensemble approach by using deep convolutional neural networks models for breast histological images classification tested on the BreakHis dataset. The proposed approach showed a significant increase of performances compared to their base learners. Thus, seven deep learning architectures (VGG16, VGG19, ResNet50, Inception V3, Inception ResNet V2, Xception, and MobileNet V2) were trained using fivefold cross-validation. Thereafter, deep end-to-end heterogeneous ensembles of two up to seven models were constructed based on three selection criteria’s (by accuracy, by diversity, and by both accuracy and diversity) and combined with two voting methods: majority voting by tacking the mode of the distribution of the predicted labels, and weighted voting by taking the average of predicted probabilities. Results showed the effectiveness of deep end-to-end ensemble learning techniques for histopathological breast cancer images classification since the ensembles designed using weighted voting with the selection by accuracy strategy method exceeded the ones designed using the selection by diversity or by accuracy and diversity strategies. The accuracy values of the proposed approach have shown a significant amelioration compared to the least performing base learner used as a baseline ResNet 50 with an accuracy increased from 78.14%, 78.57%, 82.80 and 79.43% to 93.8%, 93.4%, 93.3%, and 91.8% through the BreakHis dataset's four magnification factors: 40X, 100X, 200X, and 400X respectively.

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Data availability

The used dataset is the public histological dataset Breakhis dataset referenced by the reference [33] and cited in the threats of validity external validity line 2.

Code availability

Not applicable.

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Acknowledgements

This work was conducted under the research project “Machine Learning based Breast Cancer Diagnosis and Treatment”, 2020-2023. The authors would like to thank the Moroccan Ministry of Higher Education and Scientific Research, Digital Development Agency (ADD), CNRST, and UM6P for their support.

Funding

This study was funded by Mohammed VI polytechnic university at Ben Guerir Morocco.

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Authors and Affiliations

  1. College of Computing, Mohammed VI Polytechnic University, Benguerir, Morocco

    Hasnae Zerouaoui

  2. Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco

    Omar El Alaoui & Ali Idri

  3. Mohammed VI Polytechnic University, Benguerir, Morocco

    Ali Idri

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  1. Hasnae Zerouaoui
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  2. Omar El Alaoui
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Correspondence to Hasnae Zerouaoui.

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Zerouaoui, H., Alaoui, O.E. & Idri, A. New design strategies of deep heterogenous convolutional neural networks ensembles for breast cancer diagnosis. Multimed Tools Appl 83, 65189–65220 (2024). https://doi.org/10.1007/s11042-023-18002-0

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