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

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. 1. Background Studies on cellular morphology and phenotypic characteristics are integral to multidisciplinary research areas, including disease modeling and the mechanosensitive of biological samples. The analysis of cellular structure is crucial for understanding cell integrity and functionality, particularly in the context of neuroblastoma cells. In this study, fluorescence microscopy was used to collect data from SH-SY5Y neuroblastoma cells, which were stained using a live/dead assay kit (Ethidium Homodimer and Calcein AM). This methodology facilitates the assessment of cell viability and structural integrity, allowing for a comprehensive analysis of cell shapes. The importance of accurate cell shape classification extends beyond basic research; it has significant implications for clinical applications, including diagnostics and therapeutic strategies. The development of advanced predictive modeling techniques to classify cell shapes can enhance our understanding of cellular behavior and improve decision-making in research and clinical settings. Furthermore, the findings from sensitivity analysis and scenario testing validate the robustness of the proposed framework, reinforcing its potential as a scalable and adaptable tool for data-driven decision-making in cell analysis.