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- W4318003410 abstract "Brain hemorrhage is a life-threatening problem that happens by bleeding inside human head. In this study, computed tomography (CT) scan images have been used to classify whether the case is hemorrhage or non-hemorrhage. Different convolutional neural network (CNN) models have been observed along with some pre-trained deep learning models such as VGG16, VGG19, ResNet150, ResNet152 and InceptionV3. Pre-trained models have performed well on the dataset but all of them are heavyweight architectures in terms of number of total parameters. But the proposed model is a lightweight architecture as well as a well performing one. After evaluating the model performance, it has been observed that the proposed model gave 96.67% accuracy, 97.08% sensitivity and 96.25% specificity which is the best among other custom CNN models." @default.
- W4318003410 created "2023-01-26" @default.
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- W4318003410 date "2023-01-01" @default.
- W4318003410 modified "2023-09-30" @default.
- W4318003410 title "Classification of Brain Hemorrhage Using Deep Learning from CT Scan Images" @default.
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- W4318003410 doi "https://doi.org/10.1007/978-981-19-7528-8_15" @default.
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