ACE-CCO: A Two-Stage Deep Learning Method for Automatic Concentration Estimation of Chicken Coccidia Oocysts

Chicken coccidiosis is a common and serious parasitic disease in poultry, and vaccination is an effective preventive measure. To ensure the quality of vaccine products, it is critical to accurately identify chicken coccidia species and count the number of oocysts. Currently, the most commonly used method relies on experienced professionals performing morphological identification and manual counting under a microscope. This process is highly subjective and often leads to inconsistent counting results. Although deep learning-based coccidia automatic counting methods have improved efficiency and stability, further enhancement in accuracy and process automation is still needed. To address these challenges, this paper proposes ACE-CCO, a two-stage deep learning method for automatic concentration estimation of chicken coccidia oocysts in vaccines. First, an image processing-based frame line recognition algorithm (FLRA) is designed to accurately locate the counting area in the hemocytometer. Next, a two-stage deep learning counting algorithm is developed to enhance counting accuracy. Finally, user-friendly software is developed to support one-click batch counting and statistical analysis, improving process automation. Comparative experiments with manual counting in real vaccine assessment scenarios show that the mean relative percentage difference (MRPD) of ACE-CCO for six chicken coccidia types is below 1.5%, and the counting speed is more than six times faster. The results demonstrate the superior accuracy, efficiency, stability, and practicality of ACE-CCO, greatly reducing reliance on operator experience and providing reliable technical support for vaccine quality assessment.