Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28880, first published .
Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation

Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation

Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation

Journals

  1. Abdullah I, Javed A, Malik K, Malik G. DeepInfusion: A dynamic infusion based-neuro-symbolic AI model for segmentation of intracranial aneurysms. Neurocomputing 2023;551:126510 View
  2. Farhad M, Masud M, Beg A, Ahmad A, Ahmed L. A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques. Applied Sciences 2023;13(11):6582 View
  3. Shao J, Pan Y, Kou W, Feng H, Zhao Y, Zhou K, Zhong S. Generalization of a Deep Learning Model for Continuous Glucose Monitoring–Based Hypoglycemia Prediction: Algorithm Development and Validation Study. JMIR Medical Informatics 2024;12:e56909 View
  4. Nath-Ranga R, Absil O, Christiaens V, Garvin E. Machine learning for exoplanet detection in high-contrast spectroscopy. Astronomy & Astrophysics 2024;689:A142 View
  5. Mata-Castillo M, Hernández-Villegas A, Gordillo-Castillo N, Díaz-Román J. Systematic review of artificial intelligence methods for detection and segmentation of unruptured intracranial aneurysms using medical imaging. Medical & Biological Engineering & Computing 2025 View

Conference Proceedings

  1. Tjuatja F, Samosir A, Lucky H, Meiliana . 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). Evaluating Deep Learning Algorithms for Aneurysm Detection Using CNN and VGG-16 with Random Forest Integration View