Deep Neural Network based Abdominal Aortic Aneurysm Identification with PSO Optimization
S.Anandh, R. Vasuki1, Raid Al Baradie2

1S.Anandh, Research Scholar, Department of Biomedical Engineering, Bharath University, Chennai, India.
2Dr. R. Vasuki, Professor and Head, Department of Biomedical Engineering, BharathUniversity, Chennai, India.
3Dr. Raid Al Baradie, Associate Professor, Department of Medical Lab, Majmaah University, Kingdom of Saudi Arabia.
Manuscript received on December 14, 2019. | Revised Manuscript received on December 24, 2019. | Manuscript published on January 10, 2020. | PP: 3209-3215 | Volume-9 Issue-3, January 2020. | Retrieval Number: C9124019320/2020©BEIESP | DOI: 10.35940/ijitee.C9124.019320
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Converting the ongoing advancement of abdominal aortic aneurysm (AAA) development and rebuilding information for prescient treatment needs a significant and computational perspective demonstration system. Also abdominal aortic aneurysm is fatal and rupture hence an effective treatment is needed. The aim of this research work is to develop an algorithm that focuses on the accurate detection of the AAA image. In this proposed work, the input AAA images preprocessed to transform the RGB format into gray scale image using adaptive filter, also the pixels which are corrupted by noise is too determined. Then watershed segmentation is applied before extracting the highlighted feature from AAA images. The features of AAA are extracted by genetic algorithm. After the extraction, the best features are selected by using particle swarm optimization and finally for classification and recognition, deep neural network classifier is applied. The proposed system is appropriate to accomplish our aim in foreseeing the AAA progress and in figuring the propagation vulnerability. The performance of our system is measured using accuracy, precision, f-score and computation time are utilized. The comparative analysis of the outcomes showed the significant performance of the proposed approach over the existing SVM and CNN classifier.
Keywords:  Adaptive Median Filter, Watershed Transform, Genetic Based Algorithm, Particle Swarm Optimization (PSO) and Deep Neural Network (DNN).
Scope of the Article:  Deep Learning