# Combination Pattern Method Using Deep Learning for Pill Classification
## 6 Conclusions  
In this study, we introduced a deep learning-based pill-classification system leveraging Multi-Combination Pattern Labeling (MCPL) to enhance the identification of complex-shaped pills such as hexagons and pentagons. While our approach improved classification accuracy significantly, achieving about 35% accuracy for these shapes, there is room for further enhancement. The MCPL system, integrating algorithms like KAZE, ORB, Shi-Tomasi, and Harris, proved effective in refining feature detection, thus improving the system's accuracy and potentially reducing medication errors due to misclassification. Moreover, image quality variations such as blur and noise were recognized as crucial factors affecting performance, suggesting the need for robust preprocessing techniques to standardize input data and enhance the system's reliability. This study not only pushed the boundaries of current pill-classification systems by employing advanced deep learning methods \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Base** & \begin{tabular}{c} **Shi-Tomasi** \\ **+ KAZE** \\ \end{tabular} & \begin{tabular}{c} **Shi-Tomasi** \\ **+ ORB** \\ \end{tabular} & \
\begin{tabular}{c} **Harris** \\ **+ KAZE** \\ \end{tabular} &
\begin{tabular}{c} **Harris** \\ **+ ORB** \\ \end{tabular} \\ \hline Detection Accuracy : Hexagon & 29\% & 34.6\% & 32\% & 32.2\% & 31.5\% \\ \hline Detection Accuracy : Pentagon & 32\% & 35.3\% & 34.2\% & 33.8\% & 33.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative experimental results of the combination of feature-extraction algorithm and proposed method. Figure 12: Experimental results of MCPL-based pill detection. but also highlighted several areas for further investigation to optimize performance and expand usability. In future research, we aim to integrate the latest advancements in deep learning architectures to enhance the classification accuracy of complex-shaped pills. We plan to experiment with newer object-detection models, such as improved versions of YOLO and EfficientDet, comparing these against our current MCPL-based system to quantify any performance improvements. Additionally, recognizing the critical impact of image quality on classification success, we will conduct a systematic study to evaluate how variations in image quality--such as blur and noise--affect the system's performance. This will include testing advanced preprocessing techniques to standardize input data quality. To clarify and expand upon the dataset used, we will provide a detailed subsection in our study that describes the dataset's publicly available or proprietary sources and analyze the distribution of different pill types better to understand their impact on our system's effectiveness. Moreover, we will undertake an ablation study to distinctly showcase the contribution of each algorithmic component to the overall system efficacy. Finally, our method will be benchmarked against other state-of-the-art pill-classification systems to place our findings in a broader context and outline our system's specific limitations and challenges, setting the stage for ongoing improvements in automated pill identification. Conceptualization, S.K. and S.-Y.I. ; methodology, S.-Y.I. ; software, J.-S.K. ; validation, S.K., E.-Y.P. and S.-Y.I; formal analysis, S.-Y.I. ; resources, E.-Y.P. ; data curation, J.-S.K. and S.-Y.I; writing--original draft preparation, S.K. ; writing--review and editing, S.-Y.I. ; visualization, S.-Y.I; supervision, S.-Y.I. ; project administration, S.-Y.I; funding acquisition, S.-Y.I. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1C1C2011105). This work was supported by Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation(IIIT) grant funded by the Korea government(MSIT)(IITP-2024-RS-2022-00156334).