Improving the diagnosis of ischemic CVA's through CT scan with neural networks

Luís Ribeiro, António E B Ruano, M. Graça Ruano, Pedro Ferreira, A. Várkonyi-Kóczy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Technological and computing evolution promoted new opportunities to improve the quality of life, in particular, the quality of diagnostic evaluations. Computerized tomography is one of the imaging equipments of diagnosis which has most benefited from technological improvements. Because of that, and due to the quality of the diagnosis produced, it is one of the most employed equipments in clinical applications. The ischaemic cerebral vascular accident (ICVA) is the pathology that confirms the frequent use of the computerized tomography. The interest for this pathology, and in general for the encephalon image analysis as a preventive diagnosis, is mainly due to the frequent occurrence of ICVAs in development countries and its social-economic impact. In this sense, we propose to evaluate the ability of artificial neural networks (ANN) for automatic identification of ICVA by means of tissue density images obtained by computerised tomography. This work employed cranioencephalon computerised tomography exams and their respective medical reports, to train ANNs classifiers. Features extracted from the images were used as inputs to the classifiers. Once the ANNs were trained, the neural classifiers were tested with data never seen by the network. At this stage we may conclude that the ANNs may significantly contribute as an ICVAs computerised tomography diagnostic aid, since among the test cases the automatic identification of ischaemic lesions has been performed with no false negatives e very few false positives.

Original languageEnglish
Title of host publicationSOFA 2007 - 2nd IEEE International Workshop on Soft Computing Applications Proceedings
Pages39-43
Number of pages5
DOIs
Publication statusPublished - 2007
Event2nd IEEE International Workshop on Soft Computing Applications, SOFA 2007 - Oradea
Duration: Aug 21 2007Aug 23 2007

Other

Other2nd IEEE International Workshop on Soft Computing Applications, SOFA 2007
CityOradea
Period8/21/078/23/07

Fingerprint

Computerized tomography
Neural networks
Classifiers
Pathology
Accidents
Image analysis
Tissue
Imaging techniques
Economics

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Cite this

Ribeiro, L., Ruano, A. E. B., Ruano, M. G., Ferreira, P., & Várkonyi-Kóczy, A. (2007). Improving the diagnosis of ischemic CVA's through CT scan with neural networks. In SOFA 2007 - 2nd IEEE International Workshop on Soft Computing Applications Proceedings (pp. 39-43). [4318302] https://doi.org/10.1109/SOFA.2007.4318302

Improving the diagnosis of ischemic CVA's through CT scan with neural networks. / Ribeiro, Luís; Ruano, António E B; Ruano, M. Graça; Ferreira, Pedro; Várkonyi-Kóczy, A.

SOFA 2007 - 2nd IEEE International Workshop on Soft Computing Applications Proceedings. 2007. p. 39-43 4318302.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ribeiro, L, Ruano, AEB, Ruano, MG, Ferreira, P & Várkonyi-Kóczy, A 2007, Improving the diagnosis of ischemic CVA's through CT scan with neural networks. in SOFA 2007 - 2nd IEEE International Workshop on Soft Computing Applications Proceedings., 4318302, pp. 39-43, 2nd IEEE International Workshop on Soft Computing Applications, SOFA 2007, Oradea, 8/21/07. https://doi.org/10.1109/SOFA.2007.4318302
Ribeiro L, Ruano AEB, Ruano MG, Ferreira P, Várkonyi-Kóczy A. Improving the diagnosis of ischemic CVA's through CT scan with neural networks. In SOFA 2007 - 2nd IEEE International Workshop on Soft Computing Applications Proceedings. 2007. p. 39-43. 4318302 https://doi.org/10.1109/SOFA.2007.4318302
Ribeiro, Luís ; Ruano, António E B ; Ruano, M. Graça ; Ferreira, Pedro ; Várkonyi-Kóczy, A. / Improving the diagnosis of ischemic CVA's through CT scan with neural networks. SOFA 2007 - 2nd IEEE International Workshop on Soft Computing Applications Proceedings. 2007. pp. 39-43
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