loading
Papers

Research.Publish.Connect.

Paper

Automated Diagnostic Model Based on Heart Tissue Isoline Map Analysis

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications; RBF Structures; Support Vector Machines and Kernel Methods

Authors: Olga Senyukova 1 ; Danuta Brotikovskaya 1 ; Svetlana Gorokhova 2 and Ekaterina Tebenkova 3

Affiliations: 1 Faculty of Computational Mathematics and Cybernetics and Lomonosov Moscow State University, Russian Federation ; 2 FSBEI FPE Russian Medical Academy of Continuous Professional Education and Research Clinical Center of JSC Russian Railways, Russian Federation ; 3 Research Clinical Center of JSC Russian Railways, Russian Federation

ISBN: 978-989-758-274-5

Keyword(s): Heart Disease Diagnostics, LV Myocardium Analysis, Isoline Map, Supervised Machine Learning, Support Vector Machine, Random Forest, Cardiac Computed Tomography.

Abstract: Automated heart disease diagnostics is an important problem, especially for tissue structure defect cases. A new approach to automated diagnostics based on supervised machine learning algorithms is described in this paper. Main heart tissue layer, left ventricle myocardium, characteristics based on isoline map analysis are utilized at feature model construction stage. Histogram-based features are also extracted for comparison with the proposed method. Feature selection using chi-squared test and information gain is performed. SVM and Random Forest classifiers are used for normal/abnormal classification of left ventricle myocardium images. Different combinations of feature models and classifiers were evaluated and promising results were achieved. Isoline map-based features demonstrated superiority over histogram-based feature model and the best F-score value was above 96% on real data.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.204.48.199

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Senyukova, O.; Brotikovskaya, D.; Gorokhova, S. and Tebenkova, E. (2017). Automated Diagnostic Model Based on Heart Tissue Isoline Map Analysis.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 360-366. DOI: 10.5220/0006518203600366

@conference{ijcci17,
author={Olga Senyukova. and Danuta Brotikovskaya. and Svetlana Gorokhova. and Ekaterina Tebenkova.},
title={Automated Diagnostic Model Based on Heart Tissue Isoline Map Analysis},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={360-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006518203600366},
isbn={978-989-758-274-5},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Automated Diagnostic Model Based on Heart Tissue Isoline Map Analysis
SN - 978-989-758-274-5
AU - Senyukova, O.
AU - Brotikovskaya, D.
AU - Gorokhova, S.
AU - Tebenkova, E.
PY - 2017
SP - 360
EP - 366
DO - 10.5220/0006518203600366

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.