Breast cancer detection from histopathology images using modified residual neural networks

• Autorzy: Gupta Varun , Vasudev Megha , Doegar Amit , Sambyal Nitigya

Identyfikatory

DOI

10.1016/j.bbe.2021.08.011

• Języki publikacji: EN

Abstrakty

EN

Breast cancer is one of the major causes of death among women worldwide. Efficient diagnosis of breast cancer in the early phases can reduce the associated morbidity and mortality and can provide a higher probability of full recovery. Computer-aided detection systems use computer technologies to detect abnormalities in clinical images which can assist medical professionals in a faster and more accurate diagnosis. In this paper, we propose a modified residual neural network-based method for breast cancer detection using histopathology images. The proposed approach provides good performance over varying magnification factors of 40X, 100X, 200X and 400X. The network obtains an average classification accuracy of 99.75%, precision of 99.18% and recall of 99.37% on BreakHis dataset with 40X magnification factor. The proposed work outperforms the existing methods and delivers state-of-the-art results on the benchmark breast cancer dataset.

Słowa kluczowe

EN

breast cancer detection; histopathology image; convolutional neural network; residual network; deep neural network; computer aided detection

PL

wykrywanie raka piersi; obraz histopatologiczny; sieć neuronowa konwolucyjna; głęboka sieć neuronowa; wykrywanie wspomagane komputerowo

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 4
• Strony: 1272--1287
• Opis fizyczny: Bibliogr. 41 poz., rys., tab., wykr.

Twórcy

autor

Gupta Varun

• Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India

autor

Vasudev Megha

• Department of Computer Science and Engineering, National Institute of Technical Teachers Training & Research, Chandigarh, India

autor

Doegar Amit

• Department of Computer Science and Engineering, National Institute of Technical Teachers Training & Research, Chandigarh, India

autor

Sambyal Nitigya

• Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala, Punjab, India

Bibliografia

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