Leveraging Faster CNN (F-CNN) for Effective Breast Cancer Classification

Abstract

Within the scope of this work, a novel classification method for the diagnosis of breast cancer that is based on deep learning is also described. In this particular instance of breast cancer, which is the most common form of cancer in females, early detection is absolutely necessary in order to get better treatment outcomes. Notwithstanding their effectiveness, traditional diagnostic techniques have drawbacks such high expenses and possible errors. The high dimensionality and instability in tumor morphology that are particular problems with breast cancer imaging are intended to be addressed by the suggested techniques. Using publically available datasets for rigorous training and validation, a bespoke deep learning model is designed and implemented, and an extensive evaluation of current deep learning methodologies is conducted as part of the research. The model's accuracy and resilience are significantly improved when compared to the performance of existing classification algorithms. To enhance diagnosis accuracy in the characterization of breast cancer, this study makes utilizes of deep learning, more especially faster convolutional neural networks. The investigation also looks at the model's clinical usefulness, providing information about how it might be incorporated into diagnostic procedures. According to the findings, it appears that the highlighted methodology has the potential to transform the diagnosis of breast cancer by offering a dependable and automated solution that can improve early detection and patient outcomes. In just 3 epochs, the model obtained a remarkable accuracy of 92% on DDSM dataset.