A survey on applying handcrafted and learned feature extraction for breast cancer classification using machine learning and deep learning algorithms
Authors/Creators
- 1. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
- 2. Institute for Industry and International Programmes, Vellore Institute of Technology, Vellore, India
Description
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.