A Systematic Review of Deep Learning Techniques for Breast Cancer Detection

Breast cancer is an extremely serious disease that predominantly affects women. Nevertheless, the likelihood of mortality decreases significantly when the condition is detected and managed promptly during its early stages. To save the life of a female patient, it is crucial to locate the areas where cancer has spread. Genetic mutation associated with ageing and ignorance contribute to this condition. The resulting tumor might be either benign or malignant. The precise breast cancer detection and categorization pose a significant problem in the domain of medical imaging, primarily because of the intricate characteristics of breast tissues. Deep learning approaches have demonstrated considerable success in several domains, specifically in the realm of medical imaging, owing to their capability for autonomous feature extraction. In this work, many different deep learning models that are used in the detection of breast cancer are analyzed, and an evaluation of how well these models perform is presented.