Customized VGG16: Enhancing Breast Cancer Diagnosis Through Deep Learning Techniques
Rajesh Kumar Gouda, Debendra Muduli, Shubhajit Das, Satya Prakash Sahu, Pratik Kumar Sahu, Santosh Kumar Sharma · 2024
Breast cancer is a major worldwide health concern, which emphasizes the necessity for accurate and effective diagnostic instruments. Using deep learning techniques, this study suggests an automated strategy for detecting breast cancer. We used a pre-trained VGG16 model enhanced with extra layers to train a binary classifier using a dataset that included pictures of both benign and malignant breast tissue. The model performs well, with remarkable recall, F1-score metrics, and precision for both benign and malignant classifications, in addition to it surprisingly 98% total accuracy. These findings gives vital assistance for early identification and care. Using advanced computational methods, our work adds to current initiatives to enhance patient outcomes and diagnostic precision in breast cancer treatment.