Deep Learning Based Breast Cancer Detection With Web Application

Breast cancer is one of the common cancers for
women in the world. It is also a leading cause of cancer-related
deaths.Early and accurate detection can improving the chances
of recovery for patients.In hand ,it also depends a lot of the
experience for the pathologist.Recent progress in AI and deep
learning has made computer diagnosis more effective.Neural
Networks or CNNs can automatically find patterns in complex
medical images.Deep learning methods have also shown promise
in classifying.ResNet-based models can learn features better
thanks to residual connections.In this study introducing a deep
learning system designed to detect breast cancer from images.The
system classifies images into two categories Malignant and
Benign.Our framework combines MobileNetV3 and ResNet50V2
to capture types of image features that work well together.Using
crossvalidation and Test Time Augmentation TTA to increase the
reliability of predictions.Apply Explainable Artificial Intelligence
or XAI techniques to highlight the parts of the image for the
decision.At the end building a web application to allow real-time
predictions using the trained model.