Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang University, Shaoyang, Hunan, 422000, China.
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, 610209, China.
Anal Methods. 2023 Aug 10;15(31):3885-3892. doi: 10.1039/d3ay00787a.
Hyperspectral imaging (HSI), a widely used biosensing technique, has been applied to tumor detection. Rapid, accurate, and low-cost detection of blood cancer using hyperspectral technology remains a challenge. We developed a new method to discriminate blood cancer using hyperspectral imaging (HSI) and the forward searching method (FSM). Four commonly used classification models are applied for four types of blood cancer spectra recognition. The support vector machine (SVM) model with the highest recognition accuracy (94.5%) combined with HSI achieves high-precision tumor identification. For higher recognition accuracy and lower hardware barriers, based on the selection probabilities of spectral lines calculated by a multi-objective atomic orbital search method, the FSM is proposed for HSI feature selection. With the proposed method, the wavelength band range of the spectrum is reduced by at least 50%. Compared with the traditional dimensionality reduction methods, the FSM can obtain a higher accuracy rate with lower hardware requirements. These results show that our proposed method can achieve non-invasive rapid screening of blood cancers with lower hardware requirements. Therefore, HSI assisted with FSM and SVM hybrid models can be a powerful and promising tool for blood cancer detection.
高光谱成像（HSI）是一种广泛应用于生物传感技术的方法，已被应用于肿瘤检测。利用高光谱技术快速、准确、低成本地检测血液癌症仍然是一个挑战。我们开发了一种利用高光谱成像（HSI）和前向搜索方法（FSM）来区分血液癌症的新方法。我们应用了四种常用的分类模型来识别四种类型的血液癌症光谱。支持向量机（SVM）模型具有最高的识别准确率（94.5%），结合 HSI 实现了高精度的肿瘤识别。为了获得更高的识别准确率和更低的硬件障碍，我们基于多目标原子轨道搜索方法计算的谱线选择概率，提出了用于 HSI 特征选择的 FSM。通过所提出的方法，光谱的波长带宽范围至少减少了 50%。与传统的降维方法相比，FSM 可以在较低的硬件要求下获得更高的准确率。这些结果表明，我们提出的方法可以在较低的硬件要求下实现对血液癌症的非侵入性快速筛查。因此，HSI 辅助 FSM 和 SVM 混合模型可以成为血液癌症检测的有力且有前途的工具。
