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AN EFFICIENT MEDICAL IMAGE DIAGNOSIS SYSTEM USING SOFT COMPUTING TECHNIQUES

In this paper, we aim at presenting the process of building up of an accurate data mining architecture for detecting cancer at very early stages to enhance the probability of successful cure. Existing Computer Aided Diagnosis (CAD) systems make use of the association rule-based classification that suffers from some problems inherited from the Association Rule Mining (ARM) such as handling of continuous data and the support/confidence framework. Such problems lead to impreciseness in its operation and uncertainty about the results. The proposed method is a hybrid system that combines the principles of soft computing techniques such as Fuzzy Discretization, Genetic Network Programming (GNP) and GNP based Classification. Application of Fuzzy Discretization on the extracted features of the image makes it feasible to identify the potential cancer forming cells of tumors even at initial stages of their development. GNP is used for the Rule mining as it optimizes the rule extraction process and make the system autonomous and self-learning. This method of diagnosis of cancer based on Fuzzy Discretization and Rule Mining using GNP provides more accurate results in identifying the images in a tumor to be Malign (cancerous cell), Benign (not cancerous) .