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Combined Datasets For Breast Cancer Grading Based On Multi-CNN Architectures

Abstract : Breast cancer is the most leading cancer among women. Usually, pathologists have to examine the histological image slides through the whole slides tissues in different magnifications, to extract the tumor malignancy then the tumor grade. These image's interpretation is one of the time and effort consuming task to define an accurate diagnosis. Consequently, Computer-Aided Diagnosis (CAD) systems are highly demanded. However, the histological images have pervasive variability, which is a big challenge due to the variation of tissue textures and which is hard to be interpreted by the computer. For this, deep learning algorithms have been promised architectures for complex objects, but the problem of the low resource of datasets is still yet a constraint to build an efficient medical system for image classification. In this work, we propose a solution based on combining two different datasets for breast cancer grade detection. Our proposed method is about adding a new class (grade 0) to the three known classes of breast cancer grades, which make our model detect both the malignancy and the grade of the breast tumors. Furthermore, both datasets images have the same magnification factor which helps our models in avoiding overfitting problems. Our models are trained using two different convolutional neural network architectures, the ResNet50 and the MobileNet for comparing between a lightweight and heavyweight architectures. The obtained results show the best accuracy in the state-of-the-art.
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https://hal.archives-ouvertes.fr/hal-03119249
Contributor : Frédéric Davesne <>
Submitted on : Saturday, January 23, 2021 - 12:13:07 PM
Last modification on : Saturday, May 1, 2021 - 3:49:56 AM

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Adel Abdelli, Rachida Saouli, Khalifa Djemal, Imane Youkana. Combined Datasets For Breast Cancer Grading Based On Multi-CNN Architectures. Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA 2020), Nov 2020, Paris, France. pp.1-7, ⟨10.1109/IPTA50016.2020.9286653⟩. ⟨hal-03119249⟩

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