Abstract
Among the female population, breast cancer has been the most common fatal disease. Breast density, a risk factor for breast cancer, is a radiologic feature that represents the content of fibro glandular tissue relative to the area or volume of the breast. Breast density is one of the key attributes for breast cancer diagnosis to suggest screening and follow-ups. Although there have been many publications on the mammogram classification for cancer diagnosis, limited reviews have been published which include a comprehensive explanation of breast cancer density assessment using deep learning on mammographic images. We have placed particular focus on the breast density class assessment using Convolutional Neural Network (CNN) with Mammographic Image. The purpose of this work is to provide a brief study of the literature on recent developments in density-based diagnosis of breast cancer using deep learning on multi-view mammographic Image.
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Chugh, S., Goyal, S., Pandey, A., Joshi, S., Azad, M. (2021). Density-Assessment for Breast Cancer Diagnosis Using Deep Learning on Mammographic Image: A Brief Study. In: Bajpai, M.K., Kumar Singh, K., Giakos, G. (eds) Machine Vision and Augmented Intelligence—Theory and Applications. Lecture Notes in Electrical Engineering, vol 796. Springer, Singapore. https://doi.org/10.1007/978-981-16-5078-9_27
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