A survey on applying handcrafted and learned feature extraction for breast cancer classification using machine learning and deep learning algorithms
Salini S. Nair, M. Subaji · Zenodo (CERN European Organization for Nuclear Research) · 2023
In recent years, breast cancer has become a condition that affects more and more people. Because it can spread from the breast to other regions of the body, it is the second most common cause of mortality for women. Therefore, early detection is essential for effective treatment. Expert systems can assist in the precise detection and categorization of benign tumours utilising data mining and machine learning approaches, avoiding the need for needless therapies. This study examines the application of learned and hand-crafted features in deep learning and machine learning models for the detection of breast cancer. There are numerous models for computer-aided diagnostics that offer different applications for these methods. This review explores different breast cancer detection methods and compares their accuracies while summarizing their findings, challenges, and limitations. Overall, automated feature extraction and classification algorithms can assist medical practitioners in diagnosing and detecting breast cancer, improving patient outcomes.