A Fuzzy Logic System for Classification of the Lung Nodule in Digital Images in Computer Aided Detection

Digital image analysis technology suffers from imperfection, imprecision and vagueness of the input data and its propagation in all individual components of the technology including image enhancement, segmentation and pattern recognition. Furthermore, a Medical Digital Image Analysis System (MDIAS) such as computer aided detection (CAD) technology deals with another source of uncertainty that is inherent in an image-based practice of medicine. While there are several technology-oriented studies reported in developing CAD applications, no attempt has been made to address, model and integrate these types of uncertainty in the design of the system components even though uncertainty issues directly affect the performance and its accuracy. In order to tackle the problem of uncertainty in the classification design of the system two fuzzy methods are employed and are evaluated for the lung nodule CAD application. The Mamdani model and the Sugeno model of the fuzzy logic system are implemented and the classification results are compared and evaluated through ROC curve analysis and root mean squared error methods. The novelty of the study is to investigate the effect of training algorithms on the performance of the CAD system. The results reveal that the fuzzy logic system with hybrid-training is superior to the other models in terms of root-mean-squared error and ROC curve sensitivity and specificity rates.