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The division of breast cancer cells results in regions of electrical depolarisation within the breast. These regions extend to the skin surface from where diagnostic information can be obtained through measurements of the skin surface electropotentials using sensors. This technique is used by the Biofield Diagnostic System (BDS) to detect the presence of malignancy. This paper evaluates the efficiency of BDS in breast cancer detection and also evaluates the use of classifiers for improving the accuracy of BDS. 182 women scheduled for either mammography or ultrasound or both tests participated in the BDS clinical study conducted at Tan Tock Seng hospital, Singapore. Using the BDS index obtained from the BDS examination and the level of suspicion score obtained from mammography/ultrasound results, the final BDS result was deciphered. BDS demonstrated high values for sensitivity (96.23%), specificity (93.80%), and accuracy (94.51%). Also, we have studied the performance of five supervised learning based classifiers (back propagation network, probabilistic neural network, linear discriminant analysis, support vector machines, and a fuzzy classifier), by feeding selected features from the collected dataset. The clinical study results show that BDS can help physicians to differentiate benign and malignant breast lesions, and thereby, aid in making better biopsy recommendations.

The purpose of this study is to evaluate the efficiency of the Biofield Diagnostic System (BDS) as an adjunct to established diagnostic techniques such as mammography and ultrasound in differentiating benign and malignant breast lesions. The clinical trial was conducted at the Tan Tock Seng hospital, Singapore. 103 women scheduled for mammography and/or ultrasound tests participated in the study. The BDS test recorded a sensitivity of 100%, specificity of 97.6%, and an accuracy of 98.1%. The area under the ROC curve was 0.988 which was slightly lower than that of ultrasound (0.994) and slightly higher than that of mammography (0.951). The BDS test has demonstrated high sensitivity and specificity values in the studied population. The accuracy is also comparable to that of diagnostic techniques like mammography and ultrasound. Thus, it is evident that BDS can be a fast and reliable adjunct tool for getting a secondary opinion on lesions with indeterminate mammographic and sonographic results.

High-quality mammography is the most effective technology presently available for breast cancer screening. Efforts to improve mammography focus on refining the technology and improving how it is administered and X-ray films are interpreted. Computer-based intelligent system for identification of the breast cancer can be very useful in diagnosis and its management. This paper presents a comparative approach for classification of three kinds of mammogram namely normal, benign and cancer. The features are extracted from the raw images using the image processing techniques and fed to the two classifiers namely: the feedforward architecture neural network classifier, and Gaussian mixture model (GMM) for comparison.. Our protocol uses, 360 subjects consisting of normal, benign and cancer breast conditions. We demonstrate a sensitivity and specificity of more than 90% for these classifiers.

Breast cancer is the most frequently occurring malignancy in women. It is characterized by a high mortality rate. For the purpose of detecting this life threatening disease, research efforts are being made worldwide to exploit new technologies, to improve the detection accuracy of current devices and to develop new detection devices, comprehensive diagnostic procedures, and protocols. One such technology that is gaining popular attention over the recent years is the usage of electrical characteristics of the breast tissue to differentiate normal and cancerous tissues. Most of the devices using this technology are currently being used as adjunct diagnostic tools to improve the detection accuracy of established techniques like mammography and ultrasound. Also, early detection of breast cancer can help save many thousands of lives every year and can also reduce unnecessary healthcare expenditure caused by advanced stage treatment options. Hence, more research is also being done to adapt these devices into screening tools for early detection of breast cancer. The main objective of this review is to highlight the features of the currently available commercial devices that use this technology for breast cancer detection. The electrical behavior of normal and cancerous breast tissues is first presented. The various commercial devices that utilize electrical impedance or electropotentials for breast cancer detection are then described. Finally, conclusions and potential areas of research are highlighted.