Summary: Breast cancer research in the last decade has been tremendous and the new methods developed help in early detection, defining the stages of therapy and assess the patient's response to treatment. Some studies seem very promising and their use in the future could reduce the dose of radiation received by the patient. This article studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their advantages and disadvantages for the diagnosis of breast lesions. It has been found that the recent use of the combination of artificial neural networks in most cases gives accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.I. INTRODUCTIONBreast cancer is the second leading cause of death among women worldwide [1-4], with the risk increasing with age. Breast cancer affects not only women but also men and animals. Only 1% of all cases involve men. There are two types of breast lesions: malignant and benign. Radiologists study various characteristics to distinguish malignant tumor from benign tumor. 10–30% of breast cancer lesions are missed due to the limitations of human observers [5, 6]. The malignant tumor is in many cases misdiagnosed and its late diagnosis reduces the patient's chances of survival. Early and accurate diagnosis is essential for the patient's rapid recovery. Identifying women at risk is an important strategy to improve the number of women suffering from breast cancer. Traditionally, biopsy was used for diagnosis; today, a mammogram, breast MRI, ultrasound, BRCA tests, etc. are performed. are carried out. When a certain number of tests are performed on a patient, it becomes...... middle of paper......; Ambulgekar, H.P. (2009). “Neural network approach for diagnosing 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.-BM (2010). "Evaluating the performance of unsupervised and supervised neural networks in breast cancer detection". Proceedings of the 7th International Conference on Computer Science and Systems 2010 (INFOS-2010), March 28-30, IEEE, Cairo. pages: 1-8.[30] Yuan-Hsiang Chang; Bin Zheng; Xiao-Hui Wang; Well, W.F. (1999). "Computer-assisted diagnosis of breast cancer using artificial neural networks: comparison of backpropagation and genetic algorithms". Proceedings of the International Joint Conference on Neural Networks 1999 (IJCNN-1999), July 10-16, IEEE, Okhlahoma. pp: 3674-3679.