Automated Brain Aneurysm Classification Using Deep Convolutional Neural Networks

P. Sandhyarani and G.D. Yasaswini

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 2, 702-711

Abstract: The prevention of brain aneurysm rupture morbidity and mortality requires the early and proper identification of brain aneurysms. The present piece of work postulates an automated brain aneurysm segmentation and classification system founded on the deep convolutional neural networks, created in MATLAB 2021. The suggested system has a structured pipeline that integrates image preprocessing, handcrafted feature extraction, and deep learning-based classification to enhance the diagnostic performance. Firstly, the RGB images of the brain are turned into grayscale images and denoised with a hybrid median filter with wiener estuarization. Contrasts in images are then increased to emphasize on anatomical features. The local binary patterns (LBP) features are computed on the basis of circular neighborhoods and a Fuzzy-LBP-based segmentation scheme is implemented to precisely locate aneurysm location. The refinement of post-segmentation is performed with the help of morphological operations and thresholding, and the features of Gray-Level Co-occurrence Matrix (GLCM) are applied to describe the information on texture. An aneurysm classification into the various categories is then done automatically using a deep CNN model. The accuracy, specificity and execution time metrics are used to measure the suggested approach. The experimental results show better performance than the other existing methods and achieve an accuracy of 94.49, specificity of 91.42 with an execution time of 30.94 s. These results prove the usefulness of the suggested automated system in assisting the early and valid diagnosis of brain aneurysms.

Keywords: Brain Aneurysm Detection; Deep Learning; Convolutional Neural Network (CNN) (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST2613347 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST2613347/IJSRST2613347 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i2:id:1506

DOI: 10.32628/IJSRST2613347

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().