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

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Computational Science and Its Applications – ICCSA 2022 (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13376))

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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.

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Acknowledgement

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.

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Authors and Affiliations

  1. Modeling, Simulation and Data Analysis, Mohammed VI Polytechnic University, Benguerir, Marrakech-Rhamna, Morocco

    Fatima-Zahrae Nakach, Hasnae Zerouaoui & Ali Idri

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

    Ali Idri

Authors
  1. Fatima-Zahrae Nakach
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  2. Hasnae Zerouaoui
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  3. Ali Idri
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Correspondence to Ali Idri .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Universidad de Málaga, Malaga, Spain

    Eligius M. T. Hendrix

  4. Monash University, Clayton, VIC, Australia

    David Taniar

  5. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

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Nakach, FZ., Zerouaoui, H., Idri, A. (2022). Random Forest Based Deep Hybrid Architecture for Histopathological Breast Cancer Images Classification. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13376. Springer, Cham. https://doi.org/10.1007/978-3-031-10450-3_1

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  • DOI: https://doi.org/10.1007/978-3-031-10450-3_1

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