Deep Learning for Breast Cancer Diagnosis
Author Information
Author(s): Pérez-Núñez Jhelly-Reynaluz, Rodríguez Ciro, Vásquez-Serpa Luis-Javier, Navarro Carlos
Primary Institution: Facultad de Ingeniería de Sistemas e Informática, Universidad Nacional Mayor de San Marcos (UNMSM), Lima 15081, Peru
Hypothesis
This review aims to evaluate several convolutional neural network (CNN) models applied to breast cancer detection.
Conclusion
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.
Supporting Evidence
- CNN models with advanced architecture and greater depth exhibit high accuracy and sensitivity in image processing.
- CNN variants that integrate transfer learning proved particularly effective.
- Challenges include the need for large, labeled datasets and significant computational resources.
Takeaway
This study looks at how computers can help doctors find breast cancer using special programs that learn from pictures. It shows that these programs can be really good at spotting problems.
Methodology
Using PRISMA methodology, this review examines studies that focus on deep learning techniques, specifically CNN, for breast cancer detection.
Potential Biases
There was no detailed assessment of the risk of bias in the selected studies.
Limitations
The heterogeneity of the included studies makes direct comparison and synthesis of the results challenging.
Digital Object Identifier (DOI)
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