TRANSFER LEARNING-DRIVEN DEEP NEURAL NETWORK FRAMEWORK FOR EARLY AND ACCURATE BREAST CANCER DETECTION

Breast cancer continues to be one of the world’s leading causes of death for women and early detection is essential to increasing survival rates and simplifying treatment. Even though they work well traditional diagnostic methods frequently require a lot of time and resources and are prone to human interpretation errors. Although training deep neural networks from scratch requires large-scale annotated datasets which are frequently unavailable in medical domains recent developments in deep learning have demonstrated great promise in medical image analysis. This work suggests a Transfer Learning-Driven Deep Neural Network Framework for early and precise breast cancer detection in order to overcome this difficulty. The framework makes use of pre-trained convolutional neural network (CNN) architectures that have been refined on publicly accessible mammography and histopathological datasets including VGG19 ResNet and DenseNet. To improve model generalization and lessen overfitting extensive preprocessing procedures like image normalization noise reduction and data augmentation are used. The suggested method outperforms traditional CNNs trained from scratch with accuracy exceeding 97% precision and recall above 96% and an F1-score of 0. 97 according to experimental evaluations on benchmark datasets like BreakHis and CBIS-DDSM. The findings demonstrate how transfer learning can enable reliable data-efficient breast cancer detection systems opening the door for clinical applications with limited resources and real-time capabilities