Breast cancer detection using ml and flask

Breast cancer is a serious health issue that affects people all over the world. The importance of early detection in enhancing patient outcomes emphasizes the demand for precise and effective diagnostic technologies. This study uses convolutional neural networks (CNNs) to introduce a unique method for detecting breast cancer. The suggested solution makes use of deep learning algorithms’ strong ability to automatically extract useful information from mammography pictures.A sizable dataset of annotated mammograms that was split into training, validation, and testing sets was used in the study. These datasets were used to create and train the CNN architecture, which made use of several methods including data augmentation and transfer learning to increase the model’s resilience and generalizability. Extensive testing and assessment were done in comparison to other state-of-the-art methods and the suggested CNN-based methodology. The findings showed that the CNN model outperformed conventional techniques in diagnosing breast cancer, achieving high accuracy, sensitivity, and specificity. The suggested method demonstrated resistance to noise and changes in picture quality. These findings offer encouraging proof that CNN-based breast cancer detection can be a useful clinical tool, assisting medical practitioners in establishing precise diagnoses and enhancing patient outcomes.