Current medical imaging · 2025
Detection of Sub-acute Brain Injury and Hypoxic-ischemic Encephalopathy using I2C2-WGO and CO-GW-RNN.
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Kumar Priyan Malarvizhi, Korani Wael, Shahwar Tayyaba, C Gokulnath
Аннотация
BACKGROUND: Hypoxic-ischemic encephalopathy (HIE) is a brain injury that is caused by improper oxygen/blood flow. None of the existing works have concentrated on detecting HIE based on the sub-acute injury in the brain.
OBJECTIVE: To enhance the accuracy and specificity of HIE detection, a comprehensive approach that includes SAI identification, BGT segmentation, and volume calculation will be utilized.
METHODS: This study addresses the critical challenge of detecting hypoxic-schemic encephalopathy (HIE) through advanced image processing techniques applied to brain MRI data. The primary research questions focus on the effectiveness of the proposed CO-GW-RNN method in accurately identifying HIE and the impact of incorporating segmentation and clustering processes on detection performance.
RESULTS: The proposed method achieved remarkable results, demonstrating an accuracy of 98.98% and a specificity of 98.17%, significantly outperforming existing techniques such as the RUB classifier (84.6% accuracy) and the DTL method (94.00% accuracy).
CONCLUSION: These findings validate the effectiveness of the proposed methodology in improving HIE detection in brain MRI images.
OBJECTIVE: To enhance the accuracy and specificity of HIE detection, a comprehensive approach that includes SAI identification, BGT segmentation, and volume calculation will be utilized.
METHODS: This study addresses the critical challenge of detecting hypoxic-schemic encephalopathy (HIE) through advanced image processing techniques applied to brain MRI data. The primary research questions focus on the effectiveness of the proposed CO-GW-RNN method in accurately identifying HIE and the impact of incorporating segmentation and clustering processes on detection performance.
RESULTS: The proposed method achieved remarkable results, demonstrating an accuracy of 98.98% and a specificity of 98.17%, significantly outperforming existing techniques such as the RUB classifier (84.6% accuracy) and the DTL method (94.00% accuracy).
CONCLUSION: These findings validate the effectiveness of the proposed methodology in improving HIE detection in brain MRI images.