PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Study on Automatic Classification Method of Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-scale Spatial Spectral Fusion Features
Version 1
: Received: 13 May 2024 / Approved: 13 May 2024 / Online: 14 May 2024 (08:21:17 CEST)
How to cite:
Chen, J.; Yang, J.; Wang, J.; Zhao, Z.; Wang, M.; Sun, C.; Song, N.; Feng, S. Study on Automatic Classification Method of Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-scale Spatial Spectral Fusion Features. Preprints2024, 2024050920. https://doi.org/10.20944/preprints202405.0920.v1
Chen, J.; Yang, J.; Wang, J.; Zhao, Z.; Wang, M.; Sun, C.; Song, N.; Feng, S. Study on Automatic Classification Method of Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-scale Spatial Spectral Fusion Features. Preprints 2024, 2024050920. https://doi.org/10.20944/preprints202405.0920.v1
Chen, J.; Yang, J.; Wang, J.; Zhao, Z.; Wang, M.; Sun, C.; Song, N.; Feng, S. Study on Automatic Classification Method of Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-scale Spatial Spectral Fusion Features. Preprints2024, 2024050920. https://doi.org/10.20944/preprints202405.0920.v1
APA Style
Chen, J., Yang, J., Wang, J., Zhao, Z., Wang, M., Sun, C., Song, N., & Feng, S. (2024). Study on Automatic Classification Method of Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-scale Spatial Spectral Fusion Features. Preprints. https://doi.org/10.20944/preprints202405.0920.v1
Chicago/Turabian Style
Chen, J., Nan Song and Shulong Feng. 2024 "Study on Automatic Classification Method of Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-scale Spatial Spectral Fusion Features" Preprints. https://doi.org/10.20944/preprints202405.0920.v1
Abstract
This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLME-Resnet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial and spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3\%) and Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and adjustment of treatment, by providing physicians with insights into the underlying pathological organization of gliomas.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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