## 5 Conclusions  
In this study, we proposed a deep learning-based pharmaceutical recognition system designed for real-time medication identification on mobile devices. Our system leverages ResNet101, which demonstrated superior classification accuracy (98.51%) compared to other widely used CNN architectures, including DenseNet, EfficientNet, MobileNet, SqueezeNet, and VGGNet. Through extensive experimentation, we validated the effectiveness of residual learning in enhancing model stability and interpretability. Additionally, the integration of heatmap-based visual explanations improved transparency in automated medication recognition. The proposed system was implemented in a mobile environment, ensuring accessibility and usability, particularly for elderly individuals who are at higher risk of medication errors. By optimizing real-time inference capabilities and developing a user-friendly interface, our research contributes to the growing field of AI-powered healthcare solutions. The results suggest significant potential for reducing medication-related errors and enhancing patient safety in clinical and home care settings. Recognizing the inherent operational limitations of mobile deployment scenarios, we emphasize the critical importance of algorithmic efficiency optimization and hardware resource management. Our current system employs a ResNet101 model stored in.pkl format for inference; however, future iterations will incorporate advanced model optimization techniques, including precision-aware training methods and interoperability-focused transformation frameworks (specifically TensorFlow Lite and ONNX runtime environments), aimed at reducing computational latency and optimizing memory utilization on resource-constrained devices. These performance-enhancement strategies are undergoing rigorous empirical evaluation and will be systematically integrated into subsequent platform releases to ensure functional effectiveness in computationally limited healthcare environments. Future research will focus on expanding the dataset to include a broader range of pharmaceutical variations, improving the model's robustness against real-world imaging conditions, and exploring the integration of multimodal data sources, such as textual drug information and barcode recognition, to further enhance system performance. By continu \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Accuracy** &
\begin{tabular}{l} **Inference Speed** \\ **(ms, Mobile)** \\ \end{tabular} & **Model Size (MB)** \\ \hline ResNet50 & 0.9200 & 40.76 & 105 MB \\ Resnet101 & 0.9851 & 58.72 & 181 MB \\ Resnet152 & 0.9594 & 80.49 & 244 MB \\ DenseNet169 & 0.6048 & 61.02 & 59.9 MB \\ DenseNet121 & 0.8175 & 48.55 & 34.8 MB \\ DenseNet201 & 0.7692 & 67.43 & 83.3 MB \\ EfficientNet B0 & 0.7057 & 34.28 & 24 MB \\ EfficientNet B1 & 0.8657 & 40.81 & 34.6 MB \\ EfficientNet B2 & 0.8622 & 36.77 & 39.2 MB \\ EfficientNet B3 & 0.9019 & 42.97 & 52 MB \\ EfficientNet B4 & 0.9486 & 52.17 & 80.7 MB \\ EfficientNet B5 & 0.8613 & 67.98 & 126 MB \\ EfficientNet B6 & 0.9540 & 86.26 & 176 MB \\ MobileNet V3 Large & 0.0149 & 28.35 & 18.5 MB \\ MobileNet V3 Small & 0.0137 & 28.15 & 7.67 MB \\ SqueezeNet1\_0 & 0.0025 & 23.07 & 7.13 MB \\ VGG 16 & 0.0146 & 78.35 & 62.2 MB \\ VGG 19 & 0.0063 & 95.36 & 83.6 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of accuracy, inference speed, and model efficiency. ously refining deep learning-based pharmaceutical recognition, we aim to contribute to the advancement of safe and efficient medication management. Conceptualization, H.L. ; methodology, S.K., M.C. and J.L. ; software, S.K. ; validation, S.K., M.C. and H.L. ; writing--original draft preparation, S.K. ; writing--review and editing, H.L. ; visualization, S.K. ; supervision, H.L. ; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.