Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning

被引：56

作者：

Capizzi, Giacomo ^{[1
,2
]}

Lo Sciuto, Grazia ^{[1
]}

Napoli, Christian ^{[3
]}

Polap, Dawid ^{[2
]}

Wozniak, Marcin ^{[2
]}

机构：

[1] Univ Catania, Dept Elect Elect & Informat Engn, I-95125 Catania, Italy

[2] Silesian Tech Univ, Fac Appl Math, PL-44100 Gliwice, Poland

[3] Sapienza Univ Rome, Dept Comp Control & Management Engn Antonio Ruber, I-00185 Rome, Italy

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2020年 / 28卷 / 06期

关键词：

Lung; X-ray imaging; Neural networks; Fuzzy sets; Cancer; Probabilistic logic; Fuzzy systems; Automatic pathology recognition; biomedical image processing; chest X-ray screening fuzzy-logic; probabilistic neural network; COMPUTER-AIDED DETECTION; PULMONARY NODULES; CT IMAGES; CLASSIFICATION; FEATURES; TEXTURE; SHAPE; COMBINATION; MACHINE;

D O I：

10.1109/TFUZZ.2019.2952831

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1178 / 1189

页数：12

共 44 条

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