Application Of Machine Learning And Image Processing For Breast Cancer Risk Prediction And Diagnosis

Breast Cancer(BC)is the foremost cause of death among women,every year around10 million women are affected by deadly cancer worldwide.The most common new cancer in 2020 was breast,with about 2.26 million women affected by BC.BC is a significant prevalent and increasing disease in the world.Premature detection and prevention are a way to regulate breast cancer tumors.Numerous cases are controlled by the initial recognition and diminution of the death rate among women.Several research works have been done on breast cancer.The novel approach that is used in research in machine learning.Machine learning(ML)algorithms such as decision trees,KNN,SVM,and so on.provides the better performance result in their research field.But in modern times,an innovative developed method is used to classify BC.Machine learning algorithms can aid to discover potential patterns of diseases and symptoms based on this structured and unstructured case information.In epidemiology,this is the first prospective study on breast cancer disease in the community free movement women,and the related risk factors can be recognized.The key contents are as follows:Firstly,ML techniques such as Support Vector Machines(SVM),K Nearest Neighbor(KNN),and other ML techniques were used in the prior research.These methods are not adequate for effective BC prediction due to insufficient test data.In our research,Convolutional Neural Network(CNN)has been proposed to increase the efficiency of BC prediction and get better accuracy results.Secondly,the prediction method of BC disease based on CNN is proposed and the connection between CNN and unit state is tried to ensure the correct data acquisition during operation,and the prediction method based on CNN is realized.Finally,the original medical data of 569 participants in the data set are verified by experiments.The algorithm has an accuracy of nearly 97% and a 0.1158 loss score.