Microscopy is integral to medical research, facilitating the exploration of various biological questions, notably cell quantification. However, this process's time-consuming and error-prone nature, attributed to human intervention or automated methods usually applied to fluorescent images, presents challenges. In response, machine learning algorithms have been integrated into microscopy, automating tasks and constructing predictive models from vast datasets. These models adeptly learn representations for object detection, image segmentation, and target classification. An advantageous strategy involves utilizing unstained images, preserving cell integrity and enabling morphology-based classification—something hindered when fluorescent markers are used. The aim is to introduce a model proficient in classifying distinct cell lineages in digital contrast microscopy images. Additionally, the goal is to create a predictive model identifying lineage and determining optimal quantification of cell numbers. Employing a CNN machine learning algorithm, a classification model predicting cellular lineage achieved a remarkable accuracy of 93%, with ROC curve results nearing 1.0, showcasing robust performance. However, some lineages, namely SH-SY5Y (78%), HUH7_mayv (85%), and A549 (88%), exhibited slightly lower accuracies. These outcomes not only underscore the model's quality but also emphasize CNNs' potential in addressing the inherent complexities of microscopic images.

显微镜是医学研究不可或缺的一部分，有助于探索各种生物学问题，特别是细胞定量。然而，由于人为干预或通常应用于荧光图像的自动化方法，该过程耗时且容易出错，带来了挑战。作为回应，机器学习算法已被集成到显微镜中，自动化任务并从大量数据集中构建预测模型。这些模型熟练地学习对象检测、图像分割和目标分类的表示。一种有利的策略包括利用未染色的图像、保持细胞完整性并实现基于形态的分类——当使用荧光标记时，这会受到阻碍。目的是引入一种能够熟练对数字对比显微镜图像中不同细胞谱系进行分类的模型。此外，目标是创建一个预测模型来识别谱系并确定细胞数量的最佳量化。采用CNN机器学习算法，预测细胞谱系的分类模型达到了93%的惊人准确率，ROC曲线结果接近1.0，表现出稳健的性能。然而，一些谱系，即 SH-SY5Y (78%)、HUH7_mayv (85%) 和 A549 (88%) 的准确度稍低。这些结果不仅强调了模型的质量，还强调了 CNN 在解决显微图像固有复杂性方面的潜力。