Study of intracranial haematoma localisation based on improved RetinaNet
Pages 73 - 78
Abstract
Intracranial haemorrhage is described as bleeding within the skull. It is a serious cranio-cerebral disorder recognized for its high mortality and lethality rate, which usually requires urgent follow-up diagnosis and determination of the location and subtype of intracranial hemorrhagic lesions.In this study, we experimented with multiple available deep learning architectures to localize the location of hemorrhagic lesions after traumatic brain injury (ICH). To improve the probability of successful patient resuscitation. In this paper, we propose an improved model based on RetinaNet. The accuracy problem of lesion localisation is not effeactively addressed due to the complex structure of the lesion location in intracranial haemorrhage and the large variation in the morphology of the lesion for different subtypes. To address these problems, the paper then proceeds to optimise the original RetinaNet model in terms of its feature extraction network structure, training techniques and Anchor settings. Through comparison experiments, it can be found that the improved model is better than the three target detection models, Faster R-CNN, RetinaNet and YOLOv4.
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Index Terms
- Study of intracranial haematoma localisation based on improved RetinaNet
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Published In
April 2023
131 pages
ISBN:9781450399586
DOI:10.1145/3599589
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 August 2023
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- Research-article
- Research
- Refereed limited
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- JDYYZH- 2102044
Conference
ICMIP 2023
ICMIP 2023: 2023 8th International Conference on Multimedia and Image Processing
April 21 - 23, 2023
Tianjin, China
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