Breast Tumor Detection Using Machine Learning and Deep Learning Algorithm

Breast tumour is the most prevalent frequent malignancy inside the world, and women are more likely to develop one. This is the world’s second greatest cause of mortality for women. It accounts for almost 22% of new cancer cases worldwide, with a 5-year survival rate of 61%. Breast cancer kills 450,000 people globally each year, according to the WHO. It is a mammography-based the most prevalent kind of cancer in women, breast cancer, has a software detection and diagnosis method. The system is built on the Numerous Learning paradigm, which has been used in the past by our team to create medical decision support systems. Wavelet sub bands are used to extract the GLCM features. Then, from each location, characteristics resulting from lesion identification (masses and micro-calcifications) as well as textural cues are retrieved and mammography tests into “normal” and “bad” categories. The areas that caused the automated diagnosis can be emphasized when an aberrant examination record is found. To define this anomaly detector, two methodologies are considered.