A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer vision: a first course
Computer vision: a first course
Two-dimensional signal and image processing
Two-dimensional signal and image processing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Digital Image Processing
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Can ICA Help Classify Skin Cancer and Benign Lesions?
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
License plate detection based on genetic neural networks, morphology, and active contours
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Hi-index | 0.00 |
This paper presents a computer aided diagnosis system for skin lesions. Diverse parameters or features extracted from fluorescence images are evaluated for cancer diagnosis. The selection of parameters has a significant effect on the cost and accuracy of an automated classifier. The genetic algorithm (GA) performs parameters selection using the classifier of the K-nearest neighbours (KNN). We evaluate the classification performance of each subset of parameters selected by the genetic algorithm. This classification approach is modular and enables easy inclusion and exclusion of parameters. This facilitates the evaluation of their significance related to the skin cancer diagnosis. We have implemented this parameter evaluation scheme adopting a strategy that automatically optimizes the K-nearest neighbours classifier and indicates which features are more relevant for the diagnosis problem.