Internal organs, like lungs, are very often examined by the use of screening methods. For this purpose, we present an evaluation model based on a composition of fuzzy system combined with a neural network. The input image is evaluated by means of custom rules, which use type-1 fuzzy membership functions. The results are forwarded to a neural network for final evaluation. Our model was validated by using X-ray images with lung nodules. The results show the high performances of our approach with sensitivity and specificity reaching almost 95% and 90%, respectively, with an accuracy of 92.56%. The new methodology lowers the computational demands considerably and increases detection performances.
Capizzi, G., Sciuto, G.L., Napoli, C., Polap, D., Wozniak, M. (2020). Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 28(6), 1178-1189 [10.1109/tfuzz.2019.2952831].
Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning
Capizzi, Giacomo;Sciuto, Grazia Lo;
2020-01-01
Abstract
Internal organs, like lungs, are very often examined by the use of screening methods. For this purpose, we present an evaluation model based on a composition of fuzzy system combined with a neural network. The input image is evaluated by means of custom rules, which use type-1 fuzzy membership functions. The results are forwarded to a neural network for final evaluation. Our model was validated by using X-ray images with lung nodules. The results show the high performances of our approach with sensitivity and specificity reaching almost 95% and 90%, respectively, with an accuracy of 92.56%. The new methodology lowers the computational demands considerably and increases detection performances.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
