Abstract
Among all types of cancers found in women, breast cancer is having the second highest mortality rate and it is also considered to be the prime cause of high death rate. Mammographic images are often investigated by the experienced and trained radiologists to recognize the breast abnormalities like masses and micro-calcifications. This paper focuses on computer aided diagnosis to help the radiologists so that breast cancer can be detected with better accuracy. In this paper, our aim is to process the mammogram images for breast cancer affected patients using deep learning architectures. In this paper, two approaches are considered. In first approach, mammogram images are feed into various pre-trained models which are trained from scratch. In second approach, exactly same techniques are being replicated with the exception that fine tuning has been performed using transfer learning for all the models. For this work, we have considered variants of convolutional neural networks. Experimental works have performed on two different datasets and for both datasets, the results of the fine-tuned network outperforms all other approaches.
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Das, A., Das, H.S., Barman, U., Choudhury, A., Mazumdar, S., Neog, A. (2021). Detection of Breast Cancer from Mammogram Images Using Deep Transfer Learning. In: Thampi, S.M., Krishnan, S., Hegde, R.M., Ciuonzo, D., Hanne, T., Kannan R., J. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2020. Communications in Computer and Information Science, vol 1365. Springer, Singapore. https://doi.org/10.1007/978-981-16-0425-6_2
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