Abstract
Abstract: One of the top 5 disorders that cause death is intracranial hemorrhage. To relieve busy radiologists and identify patients in need of rapid treatment, we attempt to automate the process of identifying these bleeding in this research. we experimented with three different pretrained models, namely ResNet101, DenseNet121, and AlexNet, to improve the accuracy of their classification model. We observed that switching from ResNet50 to ResNet101 led to a modest increase in accuracy from 91.3% to 91.7%. However, using DenseNet121, which has a unique architecture where each layer receives collective knowledge from all previous layers, resulted in the highest accuracy of 91.8%. On the other hand, AlexNet, a well-known architecture that won the 2012 ImageNet competition, had a shorter training period but achieved a lower accuracy of only 89%.
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