Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Developing an advanced deep learning-based MR image framework for brain stroke segmentation and classification with novel activation function. |
| Συγγραφείς: |
Jesila Mol, J., Jancy, S. |
| Πηγή: |
International Journal of Neuroscience; Aug2026, Vol. 136 Issue 8, p1159-1183, 25p |
| Θεματικοί όροι: |
Deep learning, Magnetic resonance imaging, Ischemic stroke, Subroutines (Computer programs), Image segmentation, Diagnostic imaging, Image processing, Artificial neural networks |
| Περίληψη: |
Aim: Stroke is considered as one of the most prevalent causes of death and disability for the humans, although it is preventable and treatable. Earlier stroke detection and treatment management helps in enhancing the clinical outcomes, thereby significantly minimizing the risk of disease. Thus, this work presents a sophisticated deep learning-based stroke prediction framework using the Magnetic Resonance Imaging (MRI) and provides specialized and flexible diagnostic guidance.Methods: The developed stroke detection system begins by collecting the required MR images in the benchmark sources. Further, the gathered MR images are fed to the stroke lesion segmentation using the developed Region Masked Attention-based Multi-Dilated Inception Unet++ (RMA-MIUnet++), which is accurately focus on stroke-affected regions. The segmented images are acquired as the outcomes from the proposed RMA-MIUnet++ model. These segmented images are further classified in the developed Efficient InceptionV3 with Novel Activation Function (EIV3-NAF)-based stroke classification model. Results: The experimental validation is performed on the developed system by comparing it with other conventional methods. When considering the batch size at 48, the accuracy of the proposed model for dataset 1 is 97% and dataset 2 is 93.26%.Conclusion: The results achieved by the developed EIV3-NAF model clearly show enriched performance in classifying the strokes. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |