We investigate the process of induced pluripotent stem cell (iPSC) passaging. Subcultures are created by transferring cells from iPSC cultures to new growth mediums. We found that standard protocols for iPSC passaging primarily have researchers use their eyesight to determine cultures' confluencies and cell counts. With the consequences of inaccurate estimates going as far as cell death due to passaging at the suboptimal confluency, we sought to circumvent human error and develop a culture analyzing algorithm (CAA) that calculates both confluency and cell count primarily through Otsu's method. We incorporate multi-image machine learning into our CAA, improving its ability to recognize colonies as it is fed more images. In comparing our algorithm to standard protocols, we found that there was a significant percent difference between both methods when measuring the confluency and cell count of iPSC cultures. Through further refinement, we hope to streamline our CAA for large-scale use.