Comparative Evaluation of Fixed Windowing Strategies on CT Brain Images Using Multiple Deep Learning Models
1
Issued Date
2023-01-01
Resource Type
Scopus ID
2-s2.0-85190129608
Journal Title
Proceedings - 17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023
Start Page
196
End Page
200
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings - 17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023 (2023) , 196-200
Suggested Citation
Viriyavisuthisakul S., Kaothanthong N., Sanguansat P., Yamasaki T., Songsaeng D. Comparative Evaluation of Fixed Windowing Strategies on CT Brain Images Using Multiple Deep Learning Models. Proceedings - 17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023 (2023) , 196-200. 200. doi:10.1109/SITIS61268.2023.00037 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/98060
Title
Comparative Evaluation of Fixed Windowing Strategies on CT Brain Images Using Multiple Deep Learning Models
Corresponding Author(s)
Other Contributor(s)
Abstract
Window setting in CT brain images is the crucial pre-processing step to examine the abnormalities for diagnosing disease. Recently, many methods have been proposed to determine the suitable window automatically instead of fixing the window. However, fixed windowing methods may still be used in clinical practice due to their simplicity and ease of use. Here, we propose to evaluate the 45 different fixed windowing in noncontrast cranial computer tomography (NCCT) images without computer tomography perfusion (CTp). The 15 latest deep learning models are performed on all interested windows to classify between the hyperacute or acute phases of ischemic stroke and normal brain. The experiments can provide the reference fixed windowing value to optimize the deep learning model and help clinicians to choose the appropriate fixed windowing values.