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Identification of Blood Vessels from Anigography Images Using CNN


Authors : Dr. Sajja Suneel; B. Shiva Kumar; G. Siddhartha; V. Suresh

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/2se27sbj

Scribd : https://tinyurl.com/2vewhuud

DOI : https://doi.org/10.38124/ijisrt/26mar1605

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Abstract : Accurate and early detection of blood vessel block-ages is crucial for diagnosing cardiovascular diseases. This work presents a deep learning-based approach for identifying stenosis in angiography images using the YOLO (You Only Look Once) object detection algorithm integrated with a Convolutional Neural Network (CNN) framework. The proposed system is trained using a dataset of angiography images obtained from the Mendeley repository. The YOLO-CNN model is designed to detect and classify blood vessel blockades by processing image inputs and predicting bounding boxes around affected areas, along with their classification as “Stenosis” or “No Blockade.” A Django-based web application is developed to facilitate user regis-tration, dataset upload, model training, and real-time prediction. The model achieves a mean Average Precision (mAP) exceeding 90%, demonstrating robust performance in detecting various stages of vascular blockages. This automated detection system not only highlights the blockade region with bounding boxes but also calculates the blockage area, aiding medical professionals in evaluating the severity of the condition. The system ensures efficient, accurate, and user-friendly diagnostics through its web interface and can be extended to support clinical decisionmaking processes.

Keywords : Convolutional Neural Networks, Image Classification, Deep Learning, Computer Vision, Performance Evaluation

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Accurate and early detection of blood vessel block-ages is crucial for diagnosing cardiovascular diseases. This work presents a deep learning-based approach for identifying stenosis in angiography images using the YOLO (You Only Look Once) object detection algorithm integrated with a Convolutional Neural Network (CNN) framework. The proposed system is trained using a dataset of angiography images obtained from the Mendeley repository. The YOLO-CNN model is designed to detect and classify blood vessel blockades by processing image inputs and predicting bounding boxes around affected areas, along with their classification as “Stenosis” or “No Blockade.” A Django-based web application is developed to facilitate user regis-tration, dataset upload, model training, and real-time prediction. The model achieves a mean Average Precision (mAP) exceeding 90%, demonstrating robust performance in detecting various stages of vascular blockages. This automated detection system not only highlights the blockade region with bounding boxes but also calculates the blockage area, aiding medical professionals in evaluating the severity of the condition. The system ensures efficient, accurate, and user-friendly diagnostics through its web interface and can be extended to support clinical decisionmaking processes.

Keywords : Convolutional Neural Networks, Image Classification, Deep Learning, Computer Vision, Performance Evaluation

Paper Submission Last Date
30 - April - 2026

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