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Traditional cell counting in clinical and research settings often relies on hemocytometry, a manual technique that is labor-intensive and prone to human error. These limitations in precision and throughput can hinder the development of effective diagnostic and therapeutic strategies, particularly in the context of prostate cancer. Recent advances in machine learning have shown considerable promise in enhancing the accuracy and efficiency of cell enumeration. In this study, we present a novel software system for the automated counting of prostate cancer cells, integrating image processing with deep learning methodologies. Unique to our approach, the system robustly utilizes images acquired from conventional mobile phone cameras, offering a highly accessible and scalable solution. It applies a convolutional neural network (CNN) in conjunction with a selective search algorithm to accurately identify regions of interest (ROIs), followed by robust image analysis algorithms for precise cell detection and quantification. This two-stage pipeline addresses the inherent variability and extraneous content in mobile-captured images, which is a significant advancement over methods reliant on controlled microscopic environments. Experimental evaluations demonstrate that the proposed method achieves superior accuracy compared to conventional manual counting approaches. This automated framework offers a practical, scalable solution that may significantly improve the reliability and efficiency of cell counting in both research and clinical diagnostics.