Breast Cancer Detection Using Convolutional Neural Network

Abstract

This research endeavors by combining transfer learning techniques with Convolutional Neural Networks (CNNs), this study aims to improve the categorization of breast cancer. The primary objective is to improve diagnostic precision in detecting malignancies from mammographic images, ultimately impacting clinical decision-making and patient care positively. The study employs a robust methodology utilizing a diverse dataset for training and validation. Transfer learning optimizes CNNs' efficiency, fine-tuning the architecture to adapt to breast cancer detection nuances. Rigorous training-validation cycles refine the model, ensuring generalizability across diverse datasets. The automated system minimizes subjective variability, contributing to a more objective diagnostic process. Scalability is achieved by designing the model to handle large volumes of mammographic images, a critical feature for widespread implementation. The integration of CNNs and transfer learning yields promising results, demonstrating a substantial improvement in accuracy compared to existing methods. Automation significantly reduces diagnosis time, while introduced objectivity minimizes result variability. The property of scalability shows promise for broad use since it works well in managing massive amounts of images. These outcomes highlight the viability and effectiveness of the suggested strategy in improving the diagnosis of breast cancer. In conclusion, the developed model, combining CNNs and transfer learning, represents a significant advancement with the potential to revolutionize clinical decision-making and patient care, offering a more accurate, efficient, and widely applicable approach to breast cancer diagnosis.