Computer-based detection of different stages of the breast cancer using bio-potentials

Abstract

Breast cancer is the second most common malignancy in women after lung cancer. Roughly ten percent of women will develop breast cancer during their lifetime, and it is also the fifth most common cause of cancer-related death.

In Singapore, for example, breast cancer rates have increased by 25 percent over past ten years. Breast cancer is the most common cause of death by cancer in females, with almost 1,100 new cases diagnosed and 250 women die from breast cancer each year. Breast cancer accounts for a third of all female cancers. This translates to about 3 women being diagnosed with breast cancer everyday and one woman dying from this disease every other day.

The statistics presented above illustrate the significant impact of breast cancer in terms of both mortality and morbidity. However, the current diagnostic techniques had several limitations which make it less effective in screening of early stage breast cancer, especially in younger women. Therefore, there is an essential need to develop an efficient, cost-effective diagnostic technique so that the alarming rise in the number of affected women can be reduced.

Recently, there is a new technique, known as bio-potential field technique using an array of specially designed sensors which are placed strategically on the breasts to measure skin surface electrical potentials. This technique is on the concept of electrical impedance based differentiation of normal tissues or benign lesions from malignant lesions.

The aim of this research work is to study and develop the Electrical Impedance (EI) based technique as an adjunctive / standalone tool to screen patients with early stage breast cancer. There are many different EI based devices in the market such as T-Scan 2000, Biofield Diagnostic System (BDS), and Z-Tech Breast Cancer Detection System etc. In this study, the primary objective is to develop a comprehensive clinical diagnostic procedure based on BDS and artificial intelligence techniques for the early detection of tumors in the breast.