Microscopy is integral to medical research, facilitating the exploration of various biological questions, notably cell quantifcation. However, this process’s time-consuming and error-prone nature, attributed to human intervention or automated methods usually applied to fuorescent 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 classifcation. An advantageous strategy involves utilizing unstained images, preserving cell integrity and enabling morphology-based classifcation—something hindered when fuorescent markers are used. The aim is to introduce a model profcient in classifying distinct cell lineages in digital contrast microscopy images. Additionally, the goal is to create a predictive model identifying lineage and determining optimal quantifcation of cell numbers. Employing a CNN machine learning algorithm, a classifcation 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.