The Future of Breast Cancer Detection: A Review on the Integration of Cloud-Based Deep Learning Models
Keywords:
Breast Cancer, Logistic Regression, Convolutional Neural Networks, Artificial Neural Network, Mammograms, Electronic Health RecordAbstract
Breast cancer is the most common cancer in women and the leading cause of cancer death worldwide. This review paper focuses on how cloud-based deep learning models have the potential to transform medical diagnostics, especially in breast cancer detection. Advanced deep learning algorithms like logistic regression, convolutional neural networks, and artificial neural network models, combined with cloud computing, have the potential to transform breast cancer detection by greatly improving accuracy rates. The detection of breast cancer can be improved in many ways by using the new technologies available today, such as quantum convolutional neural networks and secure cloud-based diagnosis systems. The paper emphasizes the need to reduce the number of specialized healthcare center screenings to make medical care more widely available, especially in underserved or remote regions. Improvements in patient outcomes and healthcare delivery are expected due to increased sensitivity in detecting breast cancer, decreased reliance on human error in the diagnostic process, and a revolutionary effect on cancer management.
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