Classification of breast cancer from histopathology images using an ensemble of deep multiscale networks

• Autorzy: Karthik R. , Menaka R. , Siddharth M.V.

Identyfikatory

DOI

10.1016/j.bbe.2022.07.006

• Języki publikacji: EN

Abstrakty

EN

Manual delineation of tumours in breast histopathology images is generally time-consuming and laborious. Computer-aided detection systems can assist pathologists by detecting abnormalities faster and more efficiently. Convolutional Neural Networks (CNN) and transfer learning have shown good results in breast cancer classification. Most of the existing research works employed State-of-the-art pre-trained architectures for classification. But the performance of these methods needs to be improved in the context of effective feature learning and refinement. In this work, we propose an ensemble of two CNN architectures integrated with Channel and Spatial attention. Features from the histopathology images are extracted parallelly by two powerful custom deep architectures namely, CSAResnet and DAMCNN. Finally, ensemble learning is employed for further performance improvement. The proposed framework was able to achieve a classification accuracy of 99.55% on the BreakHis dataset.

Słowa kluczowe

EN

breast cancer; histopathology image; multiscale network; deep learning

PL

rak piersi; obraz histopatologiczny; sieć wieloskalowa; uczenie głębokie

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 3
• Strony: 963--976
• Opis fizyczny: Bibliogr. 64 poz., rys., tab., wykr.

Twórcy

autor

Karthik R.

• Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India

autor

Menaka R.

• Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India

autor

Siddharth M.V.

• School of Mechanical Engineering, Vellore Institute of Technology, Chennai, India

Bibliografia

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