Accurate Cell Classification in Neuronal Fluorescent Images: A Data Mining Approach

Abstract Cellular morphology can be used to identify cytoskeletal structural integrity of cells and hence shape analysis of cells is of importance to research. A comprehensive framework was developed to classify cell shapes using predictive modeling and optimization techniques. By integrating circularity and ellipsodality data cells from fluorescent images were classified based on shape. Advanced machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Neural Networks, were employed and optimized through hyperparameter tuning to enhance predictive accuracy. Sensitivity analysis was conducted to assess the impact of varying circularity and ellipsodality, while scenario testing validated the robustness of the framework under hypothetical conditions. The findings indicated that KNN other models, delivering superior accuracy and reliability. This study offers a scalable and adaptable methodology to support data-driven decision-making in cell structure prediction, addressing the pressing need for accurate cell analysis.