## 3 Experiment
### Virus Detection Algorithm
### Compared to Negative Control

For comparison with the negative control, which contains no virus, the data from the normal cell count are used as a baseline. If the percentage of normal cells in the experimental well exceeds 100%, it suggests that the experimental conditions may promote cell growth or enhance survival compared to the negative control. Conversely, a percentage below 100% indicates fewer normal cells relative to the negative control, suggesting that the experimental conditions may inhibit cell growth or lead to cell loss. ### Compared to Positive Control

For comparison with the positive control, data from the infected cell count are used, as the positive control contains the highest viral load. A percentage greater than 100% indicates more infected cells in the experimental well compared to the positive control, suggesting that the inhibitor may be ineffective or could even exacerbate the infection. Conversely, a percentage below 100% indicates fewer infected cells, suggesting that the inhibitor is effective in reducing viral infection. The lower the percentage, the more effective the inhibitor is at reducing infection. The scatter plot in Figure 11 illustrates the % viability and % remaining virus for each inhibitor. The x-axis represents the various inhibitor names, while the y-axis shows the percentage values. Blue dots represent the % viability, indicating how many cells survived in the presence of the inhibitor. Red dots represent the % remaining virus, which shows the viral presence after applying the inhibitor. For most inhibitors, a balance between viability and remaining virus is observed. Higher blue dots paired with lower red dots indicate that the inhibitor is effective at

Figure 11: Scatter plot illustrating the relationship between % cell viability and % remaining virus for each inhibitor substance, showing the impact of various inhibitors on both cell survival and virus presence. reducing viral infection while maintaining cell survival. For instance, inhibitors like IP072 and IP057 show high cell viability (blue) with low remaining virus (red), making them potentially effective candidates. On the other hand, inhibitors like IP0 show relatively low cell viability, suggesting potential cytotoxic effects or less favorable conditions for cell survival. In some cases, red dots are higher than blue dots, such as IP042, suggesting that the inhibitor may not be very effective in reducing viral presence. Similarly, substances like cells + DMSO serve as the control, showing expected high cell viability with minimal viral presence, helping to validate the performance of the other inhibitors. ### Cost and Feasibility Analysis

The cost of implementing the proposed automated image processing system primarily involves software licensing for MVTec Halcon. The standard license for MVTec Halcon is estimated at $3000. Unlike manual evaluation, which requires ongoing labor costs, the automated system minimizes recurring expenses after the initial investment.

In terms of time efficiency, the manual counting method typically takes around 5 min per sample, equating to over 83 h to process 1000 samples. In comparison, the automated algorithm completes the same task in less than 1 s per sample, requiring only 17 min for 1000 samples. This represents a reduction of over 99% in processing time, significantly increasing throughput for high-volume applications.

When evaluating long-term costs, manual evaluation can result in personnel expenses exceeding $2000 for 1000 samples at a rate of $20/hour. Over time, the automated system becomes more cost-effective due to its negligible processing time and labor independence. ### Broader Applications and Future Perspectives

The proposed image processing algorithm, while tailored for detecting chikungunya virus-infected cells, can be adapted to analyze samples for other viral infections with minimal modifications.