Improved Deep CNN Architecture based Breast Cancer Detection for Accurate Diagnosis

Breast cancer remains a significant health concern, ranking second-leading cause of cancer-related deaths among women. Early detection plays a crucial role in improving patient outcomes and survival rates. The current breast cancer classification system poses challenges for researchers and medical professionals. Histopathological image evaluation is a vital approach in clinical practice for the timely detection and diagnosis of breast cancer. However, its effectiveness is limited, making the detection of breast cancer a contentious topic in medical image analysis. This proposed system presents a comprehensive review and comparison of various state-of-the-art breast cancer analysis methods, focusing on the emerging field of Multi-Modal Radiomics and Deep CNN-based (MMRC) approaches. The MMRC methods combine the power of multimodal imaging data, such as ultrasound, MRI, and mammograms, with advanced deep CNN architectures for accurate breast cancer detection and classification. The objective is to explore the advancements in MMRC methods, their strengths, limitations, and potential for improving the efficiency and reliability of breast cancer diagnosis. By evaluating and comparing these innovative approaches, this proposed system aims to provide insights of breast cancer detection in achieving more accurate and effective diagnoses, ultimately contributing to improved patient outcomes and reduced mortality rates.