Ahmed F. et al.: Identification and Prediction of Brain Tumor Using VGG-16 Empowered with Explainable Artificial Intelligence. International Journal of Computational and Innovative Sciences 2(2), 2023, 24–33.
Ahn J. H. et al.: Multi-view convolutional neural networks in rupture risk assessment of small, unruptured intracranial aneurysms. Journal of Personalized Medicine 11(4), 2021, 239.
DOI: https://doi.org/10.3390/jpm11040239
Al Okashi O. M. et al.: An ensemble learning approach for automatic brain hemorrhage detection from MRIs. 12th International Conference on Developments in eSystems Engineering – DeSE, IEEE, 2019.
DOI: https://doi.org/10.1109/DeSE.2019.00172
Belaid O. N., Loudini M.: Classification of brain tumor by combination of pre-trained VGG16 CNN. Journal of Information Technology Management 12(2), 2020, 13–25.
Chellapandi B., Vijayalakshmi M., Chopra S.: Comparison of pre-trained models using transfer learning for detecting plant disease. International Conference on Computing, Communication, and Intelligent Systems – ICCCIS, IEEE, 2021.
DOI: https://doi.org/10.1109/ICCCIS51004.2021.9397098
Chen G. et al.: Automated computer-assisted detection system for cerebral aneurysms in time-of-flight magnetic resonance angiography using fully convolutional network. BioMedical Engineering OnLine 19(1), 2020, 1–10.
DOI: https://doi.org/10.1186/s12938-020-00770-7
Duan H. et al.: Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks. Biomedical Engineering Online 18, 2019, 1–18.
DOI: https://doi.org/10.1186/s12938-019-0726-2
Ghaleb Al-Mekhlafi Z. et al.: Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features. International Journal of Intelligent Systems 2023, 8616939.
DOI: https://doi.org/10.1155/2023/8616939
Ghosh S., Chaki A., Santosh K. C.: Improved U-Net architecture with VGG-16 for brain tumor segmentation. Physical and Engineering Sciences in Medicine 44(3), 2021, 703–712.
DOI: https://doi.org/10.1007/s13246-021-01019-w
Gurunathan A., Krishnan B.: Detection and diagnosis of brain tumors using deep learning convolutional neural networks. International Journal of Imaging Systems and Technology 31(3), 2021, 1174–1184.
DOI: https://doi.org/10.1002/ima.22532
Hossain T. et al.: Brain tumor detection using convolutional neural network. 1st international conference on advances in science, engineering and robotics technology – ICASERT, IEEE, 2019, 1–6.
DOI: https://doi.org/10.1109/ICASERT.2019.8934561
Kim H. C. et al.: Machine learning application for rupture risk assessment in small-sized intracranial aneurysm. Journal of Clinical Medicine 8(5), 2019, 683.
DOI: https://doi.org/10.3390/jcm8050683
Liu W., Yu L., Luo J.: A hybrid attention-enhanced DenseNet neural network model based on improved U-Net for rice leaf disease identification. Frontiers in Plant Science 13, 2022, 922809.
DOI: https://doi.org/10.3389/fpls.2022.922809
Liu X. et al.: Deep neural network-based detection and segmentation of intracranial aneurysms on 3D rotational DSA. Interventional Neuroradiology 27(5), 2021, 648–657.
DOI: https://doi.org/10.1177/15910199211000956
Minarno A. E. et al.: Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine. JOIV: International Journal on Informatics Visualization 6(2), 2022, 404–410.
DOI: https://doi.org/10.30630/joiv.6.2.991
Mujahid M. et al.: An Efficient Ensemble Approach for Alzheimer’s Disease Detection Using an Adaptive Synthetic Technique and Deep Learning. Diagnostics 13(15), 2023, 2489.
DOI: https://doi.org/10.3390/diagnostics13152489
Nakao T. et al.: Deep neural network‐based computer‐assisted detection of cerebral aneurysms in MR angiography. Journal of Magnetic Resonance Imaging 47(4), 2018, 948–953.
DOI: https://doi.org/10.1002/jmri.25842
Ofori M.: Transfer-Learned Pruned Deep Convolutional Neural Networks for Efficient Plant Classification in Resource-Constrained Environments. Masters Theses & Doctoral Dissertations 371, 2021.
Shahzad R. et al.: Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning. Scientific Reports 10(1), 2020, 21799.
DOI: https://doi.org/10.1038/s41598-020-78384-1
Stember J. N. et al.: Convolutional neural networks for the detection and measurement of cerebral aneurysms on magnetic resonance angiography. Journal of Digital Imaging 32, 2019, 808–815.
DOI: https://doi.org/10.1007/s10278-018-0162-z
Ueda D. et al.: Deep learning for MR angiography: automated detection of cerebral aneurysms. Radiology 290(1), 2019, 187–194.
DOI: https://doi.org/10.1148/radiol.2018180901
Yuan W. et al.: DCAU-Net: Dense convolutional attention U-Net for segmentation of intracranial aneurysm images. Visual Computing for Industry, Biomedicine, and Art 5(1), 2022, 1–18.
DOI: https://doi.org/10.1186/s42492-022-00105-4
Zeng Y. et al.: Automatic diagnosis based on spatial information fusion feature for intracranial aneurysm. IEEE Transactions on Medical Imaging 39(5), 2019, 1448–1458.
DOI: https://doi.org/10.1109/TMI.2019.2951439
Zhou Y. et al.: Holistic brain tumor screening and classification based on DenseNet and recurrent neural network. Crimi A., Bakas S., Kuijf H., Keyvan F., Reyes M., van Walsum T. (eds): Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science 11383. Springer, Cham [https://doi.org/10.1007/978-3-030-11723-8_21].
DOI: https://doi.org/10.1007/978-3-030-11723-8_21