Advanced Technique in Breast Thermography Analysis

Abstract

Thermography is a non-invasive and non-contact imaging technique used widely in the medical arena. This paper investigates the analysis of thermograms with the use of Bio-statistical 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 multi-pronged 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 will show 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.