# Semantic Decomposition and Anomaly Detection of Tympan Membrane Endoscopic Images
## 7 Conclusions  
We decomposed high-complexity tympanic membrane endoscopic images based on clinical features and performed the task of identifying various abnormal data not reflected in the algorithm. In the existing otolaryngology, the only application of AI technology \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Evaluation Outcomes**} \\ \cline{2-4}  & **Accuracy** & **AUC** & **F1-Score** \\ \hline Unsupervised & 94.5\% & 0.92 & 0.96 \\ method & 80.4\% & 0.80 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of unsupervised and supervised learning methods. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Evaluation Outcomes**} \\ \cline{2-3}  & **Accuracy** & **AUC** \\ \hline Canny edge detection & 94.5\% & 0.92 \\ \hline Sobel edge detection & 91.9\% & 0.91 \\ \hline Original image & 91\% & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Anomaly detection performance by pre-processing method. was supervised learning. Supervised-learning-based diagnostics are difficult to use in real-world clinical situations because it is hard for the model to diagnose unlearned data. Consequently, we performed the task of detecting anomalies in tympanic membrane endoscopic images using unsupervised learning models, VAE. Unsupervised learning models find it difficult to learn complex data, such as tympanic membrane endoscopic images. Therefore, we first worked on semantically decomposing them based on clinical features. The AHE and Canny edge-detection algorithms were applied. Further, we made three kinds of tympanic membrane endoscopic images as the original image, an image highlighting only the transparency and depth of the tympanic membrane, and an image that left only the boundary of the affected area. After image processing, only normal tympanic membranes were trained in the VAE. The VAE calculated the degree to which the image distribution of the abnormal tympanic membrane differs from that of the normal tympanic membrane as an abnormality score. By applying the K-NN algorithm to the calculated abnormality score, we were able to detect anomalies with an accuracy of 94.5%. Compared to supervised learning models with test data that were not used as learning data, our method was able to detect more anomalies than supervised learning methods. In addition, we obtained higher accuracy than the results of using only the original images. Therefore, we expect our method to be easier than the existing methods applied to otolaryngology in real clinical situations. In addition, the proposed method may be a solution to the chasm phenomena in medical AI, such as the difficulties in database development and highly complex data. Future medical AI research should involve attempts to obtain clinically relevant outcomes for patients in real-world practice settings instead of simply enhancing the classification accuracy of a model based on technological advancements. Conceptualization, D.S., I.S.S., J.K., J.C. and Y.L. ; methodology, D.S. and Y.L. ; software, D.S. and Y.L. ; resources, I.S.S., J.K. and J.C.; data curation, I.S.S. and J.C.; writing--original draft preparation, D.S. and Y.L. ; writing--review and editing, I.S.S., J.C. and Y.L. ; supervision, I.S.S., J.K., J.C. and Y.L. ; project administration, D.S. and Y.L. All authors have read and agreed to the published version of the manuscript. This study was supported by a Korea University Grant (K2210761) and the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) pro-gram (IITP-2022-RS-2022-00156439) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation). This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2022-00155885, Artificial Intelligence Convergence Innovation Human Resources Development (Hanyang University ERICA)), (No.