A robust neuro-fuzzy classifier for the detection of cardiomegaly in digital chest radiographies
We present a novel procedure that automatically and reliably determines the presence of cardiomegaly in chest image radiographies. The cardiothoracic ratio (CTR) shows the relationship between the size of the heart and the size of the chest. The proposed scheme uses a robust fuzzy classifier to find...
| Authors: | , , , |
|---|---|
| Format: | article |
| Status: | Published version |
| Publication Date: | 2014 |
| Country: | México |
| Institution: | Instituto Politécnico Nacional |
| Repository: | Redalyc-IPN |
| OAI Identifier: | oai:redalyc.org:49631663004 |
| Online Access: | https://www.redalyc.org/articulo.oa?id=49631663004 |
| Access Level: | Open access |
| Keyword: | Ingeniería Cardiomegaly fuzzy classifier chest image radiographies Radial Basis Function neural network |
| Summary: | We present a novel procedure that automatically and reliably determines the presence of cardiomegaly in chest image radiographies. The cardiothoracic ratio (CTR) shows the relationship between the size of the heart and the size of the chest. The proposed scheme uses a robust fuzzy classifier to find the correct feature values of chest size, and the right and left heart boundaries to measure the heart enlargement to detect cardiomegaly. The proposed approach uses classical morphology operations to segment the lungs providing low computational complexity and the proposed fuzzy method is robust to find the correct measures of CTR providing a fast computation because the fuzzy rules use elementary arithmetic operations to perform a good detection of cardiomegaly. Finally, we improve the classification results of the proposed fuzzy method using a Radial Basis Function (RBF) neural network in terms of accuracy, sensitivity, and specificity. |
|---|