International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 13, Issue 9 (September 2026), Pages: 101-115

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 Original Research Paper

Multiple-constraint-based coronary artery disease detection via image segmentation using Spider U-Net with hybrid pre-processing techniques

 Author(s): 

Sarwar Shah Khan 1, *, Yasser Alharbi 2

 Affiliation(s):

1Department of Computer Science, Iqra National University, Swat Campus, Pakistan
2College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0002-6387-4114

 Digital Object Identifier (DOI)

 
https://doi.org/10.21833/ijaas.2026.09.010

 Abstract

The role of coronary angiography is important for visualizing coronary arteries and detecting abnormalities such as coronary artery disease (CAD). Nevertheless, its diagnostic value is often constrained by image noise, poor contrast resolution, motion artifacts, and changes in vessel curvature and orientation. To overcome these challenges, this study proposes an automated CAD segmentation and diagnosis system using angiographic X-ray images (invasive coronary angiograms). The proposed framework begins with an image preprocessing step using two hybrid noise reduction and contrast enhancement algorithms, namely, the Median Filter with Optimized Maximum Contrast and the Adaptive Median Filter with Optimized Maximum Contrast, to reduce noise without losing edge information. For coronary artery segmentation, a Spider U-Net is adopted, which complements the traditional U-Net with attention mechanisms to enhance feature saliency and focus on clinically significant regions. The proposed framework is evaluated using several statistical measures, with accuracy, precision, recall, F1-score, and Jaccard similarity coefficient values of 0.9549, 0.8510, 0.7836, 0.9008, and 0.8762, respectively. The experimental results demonstrate that the proposed framework outperforms existing methods by effectively reducing background artifacts and enhancing the clarity and precision of vascular structures, thereby improving the reliability of coronary artery disease diagnosis.

 © 2026 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

Invasive coronary angiography, Coronary artery disease, Blood vessel segmentation, Image preprocessing, Deep learning

 Article history

Received 21 March 2026, Received in revised form 21 August 2026, Accepted 19 September 2026

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

Khan SS and Alharbi Y (2026). Multiple-constraint-based coronary artery disease detection via image segmentation using Spider U-Net with hybrid pre-processing techniques. International Journal of Advanced and Applied Sciences, 13(9): 101-115

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