Probabilistic Neural Network and Genetic Algorithm for Abdominal Aorta Aneurysm Identification
S. Anandh1, R. Vasuki2, Raid Al Baradie3
1S.Anandh, Research Scholar, Department of Biomedical Engineering, Bharath University, Chennai, India.
2Dr. R. Vasuki, Professor and Head, Department of Biomedical Engineering, Bharath University, Chennai, India.
3Dr. Raid Al Baradie, Associate Professor, Department of Medical Lab, Majmaah University, Kingdom of Saudi Arabia
Manuscript received on December 13, 2019. | Revised Manuscript received on December 24, 2019. | Manuscript published on January 10, 2020. | PP: 3202-3208 | Volume-9 Issue-3, January 2020. | Retrieval Number: C9123019320/2020©BEIESP | DOI: 10.35940/ijitee.C9123.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An abdominal aorta aneurysm (AAA) can cause severe threat if it burst. Doctors can detect the presence of AAA by using abdominal ultrasound. As the treatment depends on the location and size, accuracy plays a significant role. To prevent devastating clinical outcome in this proposed work, new approaches and algorithms were used for generating the infallible result. After processing the AAA image by using notch filter, exudate based segmentation is performed and the selected features gets classified by using probabilistic neural network classifier. By using PNN classifier, accuracy and sensitivity gets enhanced in this work. The achieved accuracy is 98% and sensitivity 97.5%. While analogizing the proposed work with other existing work. It’s very facile to perform and expected target gets achieved.
Keywords: Notch Filter, Exudate Segmentation, Gabor Based Region Covariance Matrix (GRCM), Genetic feature Selection and Probabilistic Neural Network (PNN).
Scope of the Article: Neural Information Processing