CNN and Transfer Learning Model For Breast Cancer Prediction

The more common concern among woman is breast cancer. Tissue in breast is a disease that can occur cancer cell form in breast tissue.This research presents a Convolutional Neural Network (CNN) and transfer learning-based model for breast cancer prediction, integrating the power of deep learning with pre-trained architectures to improve diagnostic accuracy. By utilizing pre-trained models such as ResNet50 and VGG16 and the system effectively captures complex patterns and features from mammogram and histopathological images. Transfer learning enhances model performance by reducing training time and mitigating data scarcity issues common in medical imaging. Proposed method results demonstrate that the in breast cancer prediction achieves high accuracy(96%) in classifying malignant and benign tumors, outperforming conventional machine learning approaches. The proposed work concludes the potential of deep learning model and transfer learning in advancing automated medical diagnosis and supporting oncologists in early breast cancer detection.

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