## 3 Experiment
### Virus Detection Algorithm
By adjusting the imaging parameters, such as fluorescence detection wavelengths and morphological feature thresholds, the algorithm could be applied to infections like dengue, Zika, or influenza, where distinct cellular changes or specific markers are present. This flexibility is inherent in the design of the image segmentation and feature extraction processes, which rely on adaptable parameters. The system also holds potential for broader drug discovery applications. Its capability to analyze cellular morphology and quantify specific features can facilitate the screening of drug candidates targeting bacterial or parasitic infections. Furthermore, toxicological studies, which require accurate assessment of cellular damage or viability, could benefit from this high-throughput, accurate, and reproducible method. Future enhancements could involve expanding the imaging setup to accommodate multiplexed staining protocols, enabling the simultaneous detection of multiple cellular markers. These improvements would further broaden the algorithm's utility across a wider range of biomedical research fields. ## 6 Conclusions

This study presents the development and validation of an automated image processing algorithm for high-throughput screening of potential anti-chikungunya virus compounds. The algorithm, created using MVTec Halcon software, demonstrated high accuracy in detecting and classifying infected and uninfected cells in viral assays, with strong agreement compared to manual counts conducted by three virology experts. Statistical validation through Pearson correlation, linear regression, Bland-Altman analyses, and box plots confirmed the algorithm's reliability. In addition to validation, the algorithm was applied to screen various antiviral compounds, showcasing its potential to significantly enhance the speed and accuracy of drug discovery workflows. By automating labor-intensive and error-prone processes, this approach offers a more efficient means of screening antiviral compounds, which is crucial in the context of increasing chikungunya virus infections. This methodology holds promise for broader applications in virological research, particularly in studying other vector-borne diseases, and emphasizes the importance of integrating image processing technologies in modern biomedical research. Future work could focus on using deep learning models to train and recognize the specific shapes and characteristics of cells, viruses, and noise commonly found in well plates. This approach could improve segmentation and classification accuracy, especially in challenging cases. Additionally, integrating these advancements with optimized imaging workflows and adapting the system for low-resource environments could further expand its accessibility and utility in various research and clinical settings. Conceptualization, S.B. and G.P. ; Data curation, P.W., S.B., P.S. and D.P. ; Formal analysis, P.W., S.B. and G.P. ; Funding acquisition, G.P. ; Methodology, P.W. and S.B. ; Project administration, P.W. and G.P. ; Resources, S.B. and G.P. ; Software, P.W. and G.P. ; Supervision, S.B. and G.P. ; Validation, P.W., P.S. and D.P. ; Visualization, P.W., S.B., P.S. and D.P. ; Writing--original draft, P.W. and G.P. ; Writing--review and editing, P.W., S.B., P.S., D.P. and G.P. All authors have read and agreed to the published version of the manuscript. This research project is supported by the Ratchadapiseksomphot Endowment Fund of Chulalongkorn University, grant number CU_GI_67_004_2100_001. Not applicable. ## References

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