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- W4387348140 abstract "Convolutional Neural Network (CNN) based deep learning models are being widely used for medical image analysis. However, it is observed from empirical study that model scaling has potential to improve performance of CNN based models. In this work, a methodology for scaling CNN based models in all dimensions suchas depth, width and resolution has been proposed. The rationale behind using all the dimensions concurrently is that the model scaling with a single dimension is found to have its limitations in prediction performance. Moreover, an CNN with Model Scaling for Brain Stroke Detection (CNNMS-BSD) has been suggested. Experiments are made using different CNN based models with model scaling using brain MRI dataset. The empirical results showed that there is significant improvement in the prediction performance when CNN models are scaled in three dimensions. Therefore, model scaling can be employed in real world applications in healthcare domain to realize a Clinical Decision Support Systemthat exploits deep learning models." @default.
- W4387348140 created "2023-10-05" @default.
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- W4387348140 date "2023-06-01" @default.
- W4387348140 modified "2023-10-06" @default.
- W4387348140 title "MRI Based Automatic Brain Stroke Detection Using CNN Models Improved with Model Scaling" @default.
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- W4387348140 doi "https://doi.org/10.1109/icpcsn58827.2023.00048" @default.
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