Tracking Knowledge evolution, hotspots and future directions of breast cancer detection using deep learning: a bibliometrics review
Authors: Monica Daniela Gómez Rios, Nestor Raul Martillo Martinez, Miguel Angel Quiroz Martinez, Maikel Yelandi Leyva Vazquez
Abstract: In the medical field, it has been necessary to provide resources to detect ear-ly-stage diseases, including breast cancer. Deep learning is immersed in all aspects of medical image analysis, catapulting it as a possible dominant autonomous technology. In this systematic review, a total of 250 results were located, of which 40 were selected, for which a quantitative methodology with a descriptive basis was chosen. The objective of this bibliometric review is to analyze models in image processing for the early detection of breast cancer using deep learning. As result, digital mammography is the most effective method for detecting abnormalities in images. The research concludes that the application of CNN (Convolutional Neural Networks) is the most preferred choice of experts for medical image analysis due to its powerful pattern recognition and feature classifier.
Keywords: Breast Cancer, Deep Learning, Medical Imaging, Convolutional Neural Networks
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