A COMPREHENSIVE STUDY: INTRACRANIAL ANEURYSM DETECTION VIA VGG16-DENSENET HYBRID DEEP LEARNING ON DSA IMAGES

Main Article Content

DOI

Sobhana Mummaneni

sobhana@vrsiddhartha.ac.in

https://orcid.org/0000-0001-5938-5740
Sasi Tilak Ravi

rsasitilak0987@gmail.com

https://orcid.org/0009-0005-3342-2984
Jashwanth Bodedla

jashwanthchowdary333@gmail.com

https://orcid.org/0009-0008-6654-1076
Sree Ram Vemulapalli

vemulapallisreeram19@gmail.com

https://orcid.org/0009-0000-1916-4433
Gnana Sri Kowsik Varma Jagathapurao

kowshikjkvarma@gmail.com

https://orcid.org/0009-0009-9684-6994

Abstract

An intracranial aneurysm is a swelling in a weak area of a brain artery. The main cause of aneurysm is high blood pressure, smoking, and head injury. A ruptured aneurysm is a serious medical emergency that can lead to coma and then death. A digital subtraction angiogram (DSA) is used to detect a brain aneurysm. A neurosurgeon carefully examines the scan to find the exact location of the aneurysm. A hybrid model has been proposed to detect these aneurysms accurately and quickly. Visual Geometry Group 16 (VGG16) and DenseNet are two deep-learning architectures used for image classification. Ensembling both models opens the possibility of using diversity in a robust and stable feature extraction. The model results assist in identifying the location of aneurysms, which are much less prone to false positives or false negatives. This integration of a deep learning-based architecture into medical practice holds great promise for the timely and accurate detection of aneurysms. The study encompasses 1654 DSA images from distinct patients, partitioned into 70% for training (1157 images) and 30% for testing (496 images). The ensembled model manifests an impressive accuracy of 95.38%, outperforming the respective accuracies of VGG16 (94.38%) and DenseNet (93.57%). Additionally, the ensembled model achieves a recall value of 0.8657, indicating its ability to correctly identify approximately 86.57% of true aneurysm cases out of all actual positive cases present in the dataset. Furthermore, when considering DenseNet individually, it attains a recall value of 0.8209, while VGG16 attains a recall value of 0.8642. These values demonstrate the sensitivity of each model to detecting aneurysms, with the ensemble model showcasing superior performance compared to its individual components.

Keywords:

DenseNet, DSA, hybrid model, intracranial aneurysm, VGG16

References

Article Details

Mummaneni, S., Ravi, S. T., Bodedla, J., Vemulapalli, S. R., & Jagathapurao, G. S. K. V. (2024). A COMPREHENSIVE STUDY: INTRACRANIAL ANEURYSM DETECTION VIA VGG16-DENSENET HYBRID DEEP LEARNING ON DSA IMAGES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(1), 105–110. https://doi.org/10.35784/iapgos.5804