Prediction of Breast Cancer Using Feature Extraction-Based Methods

A substantial amount of data is needed for efficient feature extraction and pattern recognition to guarantee the robustness of a machine-learning model in differentiating between numerous classes. It becomes essential to extract useful features from existing data or improve them using augmentation techniques to avoid the requirement for more real data. Machine learning (ML) models use artificial intelligence (AI) and ML to make life easier for patients and medical professionals while handling complicated problems in clinical imaging. A very accurate automated approach has been created to identify anomalies in bone X-ray pictures. Even with limited resources, image pre-processing techniques like noise removal and contrast enhancement are essential for enhancing image quality and obtaining high diagnostic accuracy. The Gray Level Co-occurrence Matrix (GLCM) texture features, which reflect second-order statistical data about the grayscale values of adjacent pixels, are frequently used to classify images. Various tools and methodologies for organizing, evaluating, and constructing ML models have become crucial given the enormous rise in data available in the modern period. Data is the energy or oxygen of machine learning. ML algorithms must be developed and improved continuously to handle data-related difficulties. In other words, for algorithms to produce reliable and useful models, it is crucial to have a well-organized dataset with clear patterns.