Smart Tools for Spotting and Sorting Breast Cancer: Harnessing the Power of Computers and Learning Systems
Anirban Bera, Souvik Bera · Apple Academic Press eBooks · 2025
More effective therapy and higher survival rates depend on early detection and classification. Using machine learning (ML) approaches, automated breast cancer diagnosis and classification have shown great potential. Hybrid ML models combine two or more ML techniques to improve accuracy and performance. These algorithms are trained using a vast collection of patient data and images related to breast cancer. Beyond basic qualities like tumor size, shape, and texture, the model may employ deep learning (DL) approaches to extract more complex data from images. This makes it possible to conduct a more targeted examination, which might raise the accuracy of the model. After training, the model is assessed on a separate dataset to determine how well it detects and classifies breast cancer. The advantages of many approaches are integrated into hybrid ML models, which often outperform single methods.