Abstract
Deep learning is a category of machine learning algorithms and has sparked a great deal of interest in its applicability to radiography challenges due to its tremendous improvement. It is using an end-to-end learning methodology, which effectively utilizes training datasets with comprehensive clinical annotations. In this paper, we propose modified CNN methods that can effectively identify breast cancer in the investigation of mammography. In this work, tumor descriptions are required through the early training phase, and later phases require only image-level descriptors, removing the need for hardly accessible tumor descriptions. In comparison with earlier approaches, all of our modified CNNs for categorizing the mammograms with promising result in accuracies analyzed through various color schemes.
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Suganya Devi, K., Sekar, K., Singh, N., Baroi, S.J., Sah, D.K., Borahi, K. (2022). Detection of Abnormalities in Mammograms Using Deep Convolutional Neural Networks. In: Das, K.N., Das, D., Ray, A.K., Suganthan, P.N. (eds) Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6893-7_37
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