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The article presents a study on the efficiency of the biofield diagnostic system (BDS) to detect the breast cancer at Tan Tock Seng hospital in Singapore. It also examines the use of classifiers for accuracy which include back propagation network, linear discriminant analysis, and support vector machines. It concludes that BDS can help physicians in making better biopsy recommendations as they can better differentiate benign and malignant breast lesions.

The article looks at a study that aims to evaluate the efficiency of the Biofield Diagnostic System (BDS) as an adjunct to established diagnostic techniques like mammography and ultrasound in differentiating benign and malignant breast lesions. Topics covered include the features of the BDS device, eligibility of women belonging to any ethnic group who had lesions for the study, and the effect of the lesion parameters such as size and location of the lesion on the presence of cancer.

Severe Acute Respiratory Syndrome (SARS) is a highly infectious disease caused by a coronavirus. Screening to detect potential SARS infected subject with elevated body temperature plays an important role in preventing the spread of SARS. The use of infrared (IR) thermal imaging cameras has thus been proposed as a non-invasive, speedy, cost-effective and fairly accurate means for mass blind screening of potential SARS infected persons. Infrared thermography provides a digital image showing temperature patterns. This has been previously utilized in the detection of inflammation and nerve dysfunctions. It is believed that IR cameras may potentially be used to detect subjects with fever, the cardinal symptom of SARS and avian influenza. The accuracy of the infrared system can, however, be affected by human, environmental, and equipment variables. It is also limited by the fact that the thermal imager measures the skin temperature and not the body core temperature. Thus, the use of IR thermal systems at various checkpoints for mass screening of febrile persons is scientifically unjustified such as what is the false negative rate and most importantly not to create false sense of security. This paper aims to study the effectiveness of infrared systems for its application in mass blind screening to detect subjects with elevated body temperature. For this application, it is critical for thermal imagers to be able to identify febrile from normal subjects accurately. Minimizing the number of false positive and false negative cases improves the efficiency of the screening stations. False negative results should be avoided at all costs, as letting a SARS infected person through the screening process may result in potentially catastrophic results. Hitherto, there is lack of empirical data in correlating facial skin with body temperature. The current work evaluates the correlations (and classification) between the facial skin temperatures to the aural temperature using the artificial neural network approach to confirm the suitability of the thermal imagers for human temperature screening. We show that the Train Back Propagation and Kohonen self-organizing map (SOM) can form an opinion about the type of network that is better to complement thermogram technology in fever diagnosis to drive a better parameters for reducing the size of the neural network classifier while maintaining good classification accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]