Breast Cancer Detection Using ResNet50

Abstract

One of the biggest risks to women's health in the world is still breast cancer. Effective treatment and better patient outcomes depend on early and precise detection. In this work, we use a refined ResNet50 convolutional neural network (CNN) to suggest a deep learning-based method for breast cancer detection. Using the benefits of residual learning, the model is trained on breast ultrasound images to distinguish between benign and malignant tissue. Our findings show that the ResNet50 model outperforms conventional machine learning methods and achieves high classification accuracy. This study demonstrates how deep neural networks and transfer learning can improve medical image analysis by providing a dependable, automated diagnostic tool that helps radiologists diagnose patients more quickly and accurately.

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