Breast Cancer Identification and Diagnosis Techniques

Abstract

Identification of disease in humans accurately is very difficult and also important for further treatment. One of the major tasks for a doctor is the identification of the disease. Once the disease is identified then it is very easy to perform diagnosis for the patient. In this chapter, we reviewed and presented various machine learning and deep learning algorithms for disease identification. Mainly we are presenting on one of the most occurring diseases in women, that is breast cancer. Here we are presenting the various methodology and algorithms for the identification of breast cancer. Some of the methodologies and algorithms are Support Vector Machine (SVM), Biclustering mining and Adaboost Algorithm, Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Breast Imaging reporting and data system (BI-RADS), ICD-9 diagnosis codes from an existing EHR data repository, Hierarchical Attention bidirectional networks (HA-BiRNN), Clinical decision support system, Outlier Detection Algorithm (ODA).