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 SMLMER-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–spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a 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 treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.

A micro-confocal Raman spectrometer is developed using four tiled sub-gratings, each with a groove density of 1200 lines/mm, and a single optimized cylindrical lens. The system achieves a broad Raman shift coverage from 99.8 to 4701 cm⁻¹ with a spectral resolution of approximately 4 cm⁻¹, and operates without any mechanical moving parts. The cylindrical lens corrects multi-channel astigmatism, compressing the spot size to one quarter of its original dimension and improving the signal-to-noise ratio by a factor of four. The prototype successfully detects Raman signals from silicon, sodium nitrite, cyclohexane, and sodium sulfate solutions with concentrations as low as 0.01 mol/L. It also resolves inorganic mixtures, minerals including celestine, rhodonite, and calcite, as well as microplastics such as polyamide, polytetrafluoroethylene, polyethylene, and styrene-ethylene-butylene-styrene block copolymer. These results demonstrate that the spectrometer offers reliable qualitative and quantitative detection capabilities with both broad spectral bandwidth and high resolution.