Brain CT Image Processing Using U-Net Model with Data Augmentation for Detection of Ischemic and Haemorrhage Strokes
Keywords:
Brain CT Image, Image Processing, U-Net Model, Haemorrhage Strokes, Data AugmentationAbstract
Brain stroke has been causing deaths and disabilities across the globe in alarming rate. With the emergence of Artificial Intelligence (AI), there has been increased efforts in usage of it in healthcare domain. However, it is observed that deep learning models are more suitable to process medical images. Nevertheless, deep learning models cannot give same level of performance for each application in medical domain. For this reason, in this paper, we proposed a framework where U-Net model is configured appropriate and data augmentation is carried out to solve the problem of brain CT scan based automatic detection of stroke. We proposed an algorithm known as Learning based Medical Image Processing for Brain Stroke Detection (LbMIP-BSD). This algorithm exploits supervised learning using U-Net based model with data augmentation for leveraging brain stroke detection performance. Our empirical study revealed that the proposed model outperformed existing deep learning models such as baseline CNN, VGG16 and ResNet50 with highest accuracy 94.57%.
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