An Analysis of the Methods Employed for Breast Cancer Diagnosis

TL;DR

This paper reviews various diagnostic methods for breast cancer, highlighting recent advances like artificial neural networks that improve accuracy and could be applied to other diseases.

Contribution

It provides a comprehensive analysis of existing techniques for breast cancer diagnosis, emphasizing the potential of neural networks in enhancing diagnostic accuracy.

Findings

Neural networks show promising accuracy in breast cancer diagnosis.

Combination of methods improves early detection.

Some emerging techniques are still unproven but encouraging.

Abstract

Breast cancer research over the last decade has been tremendous. The ground breaking innovations and novel methods help in the early detection, in setting the stages of the therapy and in assessing the response of the patient to the treatment. The prediction of the recurrent cancer is also crucial for the survival of the patient. This paper studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their merits and de-merits for the diagnosis of breast lesion. Some of the methods are yet unproven but the studies look very encouraging. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances gives accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.