INTEGRATIVE COMPUTER-AIDED DIAGNOSTIC WITH BREAST THERMOGRAM

Abstract

Thermography is a non-invasive and non-contact imaging technique widely used in the medical arena. This paper investigates the analysis of thermograms with the use of biostatistical methods and Artificial Neural Networks (ANN). It is desired that through these approaches, highly accurate diagnosis using thermography techniques can be established. The proposed advanced technique is a multipronged approach comprising of Linear Regression (LR), Radial Basis Function Network (RBFN) and Receiver Operating Characteristics (ROC). It is a novel and integrative technique that can be used to analyze complicated and large numerical data. In this study, the advanced technique will be used to analyze breast cancer thermogram for diagnosis purposes. The use of LR shows the correlation between the variables and the actual health status (healthy or cancerous) of the subject, which is decided by using mammography. This is important when selecting the variables to be used as inputs, in particular, for building the neural network. For ANN, RBFN is applied. Based on the various inputs fed into the network, RBFN will be trained to produce the desired outcome, which is either positive for cancerous or negative for healthy cases. When this is done, the RBFN algorithm will possess the ability to predict the outcome when there are new input variables. The advantages of using RBFN include fast training, superior classification and decision making abilities as compared to other networks such as back-propagation. Next, ROC is used to evaluate the accuracy, sensitivity and specificity of the outcome of RBFN Test files. The best results obtained are an accuracy (score) rate of 80.95%, with 81.2% sensitivity and 88.2% specificity. For breast cancer diagnosis, clinical examination by experienced doctors has an accuracy rate of approximately 60–70%. Hence, the proposed method has a higher accuracy rate than the existing practice. Through the use of Bio-statistical methods and ANN, improvements are made in thermography application with regard to achieving a higher level of accuracy rate in diagnosis as compared to clinical examination. It has now become possible to use thermography as a powerful adjunct tool for breast cancer detection, together with mammography for diagnosis purposes.