AI- Based Cell Counting System

Abstract

Accurate cell counting in biomedical microscopy is a critical task for numerous applications, including disease diagnosis, drug testing, and tissue engineering. Traditional image processing methods, while computationally efficient, often struggle with complex cell morphologies, overlapping structures, and varying illumination conditions. Conversely, deep learning-based approaches can achieve high accuracy but require large annotated datasets and are computationally expensive. In this study, we propose a hybrid vision-based framework that combines classical image processing techniques with AI driven regression modelling to achieve precise and efficient cell counting. The method first employs conventional image enhancement, segmentation, and morphological operations to extract robust low-level features such as cell boundaries and regions of interest. These extracted features are then input into a regression-based learning model, trained to predict cell counts while compensating for segmentation errors and occlusions. Experimental results on multiple biomedical microscopy datasets demonstrate that the proposed hybrid approach outperforms purely classical or deep learning methods in terms of accuracy, generalization, and computational cost. This work highlights the potential of hybrid vision systems for reliable quantitative analysis in biomedical imaging.