Dataset link : https://drive.google.com/file/d/1EOH_SQEb0BV8tbVvpYbUdSmeyITQ0XT5/view?usp=share_link
This dataset contains nearly 3Lakh chest xray images which are compressed into a file.
Breast cancer detection with deep learning involves developing a machine learning algorithm that can accurately identify the presence of breast cancer in medical images such as mammograms or ultrasound scans. The goal is to provide a faster and more accurate way to detect breast cancer, which can improve early detection and potentially save lives.
The problem involves processing large amounts of medical image data and training a deep learning model to recognize patterns in the data that are indicative of breast cancer. This typically involves using convolutional neural networks (CNNs), which are specifically designed to analyze visual data.
The input to the deep learning model is a medical image, which is processed by the CNN to generate a prediction of whether the image contains signs of breast cancer or not. The output of the model is a probability score indicating the likelihood of cancer.
The accuracy of the model is evaluated by comparing its predictions to the ground truth labels (i.e. whether the image actually contains signs of breast cancer or not) and calculating metrics such as precision, recall, and F1 score.
Overall, breast cancer detection with deep learning is a challenging problem that requires expertise in both medical imaging and deep learning. However, it has the potential to significantly improve the accuracy and efficiency of breast cancer diagnosis, which can ultimately save lives.