Abstract
Glioma cells are a type of tumor cells that originate in the supportive tissue of the brain and spinal cord called glial cells. These cells can proliferate rapidly, leading to the formation of a mass or tumor in the brain or spinal cord. Glioma cells can be classified into different types based on their morphology under a microscope and the genetic alterations they undergo. Some types of glioma cells are more aggressive and have a worse prognosis than others. Treatment for glioma cells typically involves a combination of surgery, radiation therapy, and chemotherapy. In recent years, deep learning techniques have found extensive applications in the field of medical imaging, particularly in neuroimaging. Deep learning has been used to aid physicians in automatically detecting and classifying images of glioma cells to improve diagnostic accuracy and treatment effectiveness. This approach typically requires a significant amount of data to train neural networks to identify specific features and types of glioma cells. On this basis, we proposed Cascade Fusion Network (CFNet) to try to improve the accuracy of identification of glioma cells.
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Funding
This work was supported by the National Natural Science Foundation of China (Grant nos. 62002189, 62102200), the Natural Science Foundation of Shandong Province, China (No. ZR2020QF038), the 20 Planned Projects in Jinan (No.2021GXRC046), and the Excellent Teaching Team Training Plan Project of QILU UNIVERSITY OF TECHNOLOGY.
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Yuan, L. et al. (2023). An Improved Method for CFNet Identifying Glioma Cells. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_9
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