Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50

Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50

Abstract Accurate prediction of stroke risk at an early stage is essential for timely intervention and prevention, especially given the serious health consequences and economic burden that strokes can cause. In this study, we proposed a class-balanced and data-augmented (CBDA-ResNet50) deep learning...

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Bibliographic Details
Main Authors: Muhammad Asim Saleem, Ashir Javeed, Wasan Akarathanawat, Aurauma Chutinet, Nijasri Charnnarong Suwanwela, Pasu Kaewplung, Surachai Chaitusaney, Watit Benjapolakul
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Stroke prediction
Deep learning
ResNet50
Class balancing
Data augmentation
Online Access:https://doi.org/10.1038/s41598-025-07350-6
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https://doi.org/10.1038/s41598-025-07350-6

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