Abstract
Stroke is a dangerous disease with a complex disease progression and a high mortality rate just behind cancer and cardiovascular disease. To diagnose brain hemorrhage, the doctors check CT/MRI images and rely on the Hounsfield Unit to determine the region, duration and level of bleeding. Due to the increasing number of brain haemorrhages, it will put pressure on the treating doctors. Therefore, the construction of an automatic system of segmentation and identification of brain hemorrhage with fast processing time and high accuracy is essential. In this paper, we propose a new approach based on Hounsfield Unit and deep learning techniques. It not only determines the level and duration of hemorrhage but also segments the brain hemorrhagic regions on MRI images automatically. From experiments, we compared and evaluated on three neural network models to select the most suitable model for classification. As a result, the proposed method using Hounsfield Unit and Faster RCNN Inception is time-effective and high accuracy with mean average precision (mAP) of 79%.
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Phan, AC., Cao, HP., Trieu, TN., Phan, TC. (2020). Detection and Classification of Brain Hemorrhage Using Hounsfield Unit and Deep Learning Techniques. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2020. Communications in Computer and Information Science, vol 1306. Springer, Singapore. https://doi.org/10.1007/978-981-33-4370-2_20
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DOI: https://doi.org/10.1007/978-981-33-4370-2_20
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