A NOVEL VITBILSTM DEEP LEARNING FRAMEWORK FOR BRAIN HEMORRHAGE PREDICTION USING CT BRAIN IMAGES
DOI:
https://doi.org/10.25271/sjuoz.2025.13.3.1488Keywords:
Artificial Intelligenc, deep learning, Vision Transformer, BILSTM, Brain HemorrhagicAbstract
Bleeding in the surrounding tissues of the human brain is called a brain hemorrhage. This problem can lead to stroke and even death. It requires fast intervention and accurate treatment to save a patient’s life. Current state-of-the-art methodologies to detect this issue benefit from the development in the artificial intelligence field, especially its sub-filed “deep learning”. This study introduces a new deep learning-based framework to detect brain hemorrhage inside CT brain images. The proposed model is a novel hybrid model of vision transformer models and the bidirectional long short-term memory and is denoted as “ViTBiLSTM”. The study utilizes two datasets, which are different in size and challenging. The first dataset consists of 6772 CT images, while the second one contains 2500 CT images. The study also compares the original vision transformer model with the proposed one. Besides that, the study utilizes different optimizers and compares the current research with the related work. Results show that the proposed ViTBiLSTM achieves its best performance when using the RMSProp optimizer with an accuracy of 100% and 96.94% on both datasets. Comparison with the current state of the art shows that the proposed methodology’s performance exceeds the best study by 3.7% in accuracy.
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