Abstract：
In this study, we propose an Ethanol Pretreatment Gram staining method that significantly enhances the color contrast of the stain, thereby improving the accuracy of judgement, and demonstrated the effectiveness of the modification by eliminating unaided-eye observational errors with unsupervised machine learning image analysis. By comparing the traditional Gram staining method with the improved method on various bacterial samples, results showed that the improved method offers distinct color contrast. Using multimodal assessment strategies, including unaided-eye observation, manual image segmentation, and advanced unsupervised machine learning automatic image segmentation, the practicality of ethanol pretreatment on Gram staining was comprehensively validated. In our quantitative analysis, the application of the CIEDE2000, and CMC color difference standards confirmed the significant effect of the method in enhancing the discrimination of Gram staining.This study not only improved the efficacy of Gram staining, but also provided a more accurate and standardized strategy for analyzing Gram staining results, which might provide an useful analytical tool in microbiological diagnostics.
摘要：
在这项研究中,我们提出了一种乙醇预处理革兰氏染色方法，该方法显着增强了染色的颜色对比度，从而提高判断的准确性，并通过无监督机器学习图像分析消除肉眼观察误差，证明了修改的有效性。通过比较传统的革兰氏染色方法和改进的方法对各种细菌样品,结果表明，改进后的方法具有明显的颜色对比度。使用多模式评估策略，包括肉眼观察，手动图像分割，和先进的无监督机器学习自动图像分割，全面验证了乙醇预处理对革兰氏染色的实用性。在我们的定量分析中，CIEDE2000和CMC色差标准的应用证实了该方法在增强革兰氏染色的辨别方面的显着效果。本研讨不只改良了革兰氏染色的功效，而且还提供了一种更准确和标准化的策略来分析革兰氏染色结果，这可能在微生物诊断中提供有用的分析工具。
