Summary: Breast cancer research over the past decade has been extraordinary, and new methods being developed help in early diagnosis, defining stages of therapy, and in evaluating the patient's response to treatment. Some studies look very promising and their use in the future could reduce the radiation dose to the patient. This article studies various techniques used to diagnose breast cancer. Different methods are explored for their merits and demerits for breast lesion diagnosis. The recent use of the combination of artificial neural networks has been found to provide accurate results for breast cancer diagnosis in most cases, and their use can also be extended to other diseases.I. INTRODUCTION Breast cancer is the second leading cause of death among women worldwide [1-4], the risk increases with age. Breast cancer affects not only women but also men and animals. Only 1% of all cases are found in men. There are two types of breast lesions: malignant and benign. Radiologists study various characteristics to distinguish between malignant tumors and benign tumors. 10%–30% of breast cancer lesions go undetected due to the limitations of human observers [5, 6]. The malignant tumor in many cases is misdiagnosed and its late diagnosis reduces the patient's chances of survival. An early and accurate diagnosis is essential for the timely recovery of the patient. Identifying women at risk is an important strategy to increase the number of women affected by breast cancer. Conventionally, biopsy was used for diagnosis, nowadays mammography, breast MRI, ultrasound, BRCA test, etc. are performed. When multiple tests are performed on a patient this becomes…… half the paper……; Ambulgekar, H. P. (2009). “Neural network approach to diagnose breast cancer on three different datasets,” Proceedings Advances in Recent Technologies in Communication and Computing 2009 (ARTcom-2009), October 27-28, IEEE, Kottayam. pp: 893-895.[29] Belciug, S.; Gorunescu, F.; Gorunescu, M.; Salem, A.-B.M. (2010). “Performance Evaluation of Unsupervised and Supervised Neural Networks in Breast Cancer Detection.” Proceedings of the 7th International Conference on Computing and Systems 2010 (INFOS-2010), March 28-30, IEEE, Cairo. pp: 1-8.[30] Yuan-Hsiang Chang; Bin Zheng; Xiao-Hui Wang; Well, W.F. (1999). “Computer-aided diagnosis of breast cancer using artificial neural networks: comparison of backpropagation and genetic algorithms.” Proceedings International Joint Conference on Neural Networks 1999 (IJCNN-1999), 10-16 July, IEEE, Okhlahoma. page: 3674-3679.