The Challenge of Deep Learning for the Prevention and Automatic Diagnosis of Breast Cancer: A Systematic Review

Objectives: This review aims to evaluate several convolutional neural network (CNN) models applied to breast cancer detection, to identify and categorize CNN variants in recent studies, and to analyze their specific strengths, limitations, and challenges. Methods: Using PRISMA methodology, this review examines studies that focus on deep learning techniques, specifically CNN, for breast cancer detection. Inclusion criteria encompassed studies from the past five years, with duplicates and those unrelated to breast cancer excluded. A total of 62 articles from the IEEE, SCOPUS, and PubMed databases were analyzed, exploring CNN architectures and their applicability in detecting this pathology. Results: The review found that CNN models with advanced architecture and greater depth exhibit high accuracy and sensitivity in image processing and feature extraction for breast cancer detection. CNN variants that integrate transfer learning proved particularly effective, allowing the use of pre-trained models with less training data required. However, challenges include the need for large, labeled datasets and significant computational resources. Conclusions: CNNs represent a promising tool in breast cancer detection, although future research should aim to create models that are more resource-efficient and maintain accuracy while reducing data requirements, thus improving clinical applicability.