When working with biological cells, two key principles should guide the process: acceleration and accuracy. Estimating cell viability is a task that greatly benefits from methods that embody these characteristics. Traditionally, viable and dead cells have been classified manually using techniques such as cell staining with chemical reagents or fluorescence. However, these processes are limited by their reliance on manual intervention and the inherent fragility of cells. deep Through the methods introduced in this report, a third key principle is introduced: automation. Combining imaging techniques with deep learning algorithms presents a promising alternative, enabling the automation of single-cell viability classification. By leveraging deep learning and dimensionality reduction, systems can be developed to streamline and improve cell classification. To validate these systems, it is assumed that stained and unstained cells can be analyzed in a similar manner. This research aims to establish that no meaningful difference exists between stained and unstained cell images, laying a solid ground truth for the development of automated classification systems. These advancements hold significant potential for applications in cancer research, drug development, and stem cell studies.