Neural Network Approaches for Early Breast Cancer Detection

Abstract

Research on breast cancer is crucial due to its significant impact on public health, with high mortality rates underscoring the urgency for improved diagnostic methods. Early detection plays a pivotal role in enhancing treatment outcomes and reducing mortality rates. This paper addresses the pressing need for more effective early disease detection methods, particularly focusing on breast cancer diagnosis. It proposes the utilization of neural networking techniques, known for their potential to enhance accuracy and efficiency in cancer diagnosis. The study aims to provide a comprehensive overview of breast cancer detection using neural networking, emphasizing its significance in improving patient outcomes. By showcasing the effectiveness of neural network approaches, the research contributes to advancing early cancer detection efforts, aligning with global health initiatives prioritizing early diagnosis and intervention.