# CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning
## 5 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & **Type01** & **Type02** & **Type03** & **Type04** & **Type05** & **Type06** & **Type07** \\  & & **texts =** & **texts =** & **texts =** & **texts =** & **texts =** & **texts =** & **texts =** & **texts =** \\  & **116** & **160** & **21** & **130** & **37** & **73** & **130** & **Accuracy** \\ \hline \multirow{4}{*}{**CIMI**} & Correct (Average) & 100.3 & 142.9 & 20.7 & 127.4 & 36.9 & 72.2 & 128.1 & \\  & Omitted (Average) & 9.7 & 19.0 & 0.1 & 1.2 & 0.0 & 0.0 & 0.1 & \\  & Error (Average) & 6.0 & 2.3 & 0.2 & 1.4 & 0.1 & 0.8 & 1.7 & \\  & Accuracy (\%) & 86.5 & 87.1 & 98.6 & 98.0 & 99.7 & 98.9 & 98.5 & 95.3 \\ \hline \multirow{4}{*}{**Vanilla**} & Correct (Average) & 83.3 & 122.5 & 10.8 & 106.4 & 34.0 & 59.8 & 76.3 & \\  & Omitted (Average) & 14.6 & 25.8 & 9.5 & 13.5 & 0.0 & 8.9 & 38.4 & \\  & Error (Average) & 18.7 & 15.3 & 0.7 & 10.1 & 3.0 & 4.3 & 15.2 & \\  & Accuracy (\%) & 71.8 & 74.7 & 51.4 & 81.8 & 91.9 & 81.9 & 58.7 & 73.2 \\ \hline \multirow{4}{*}{**Resize**} & Correct (Average) & 98.4 & 142.5 & 19.7 & 126.2 & 36.2 & 64.0 & 101.1 & \\  & Omitted (Average) & 7.8 & 18.4 & 1.0 & 1.8 & 0.5 & 9.0 & 16.8 & \\ Pre-processed & Error (Average) & 10.4 & 2.7 & 0.3 & 2.0 & 0.3 & 0.0 & 12.0 & \\  & Accuracy (\%) & 84.8 & 86.9 & 93.8 & 97.1 & 97.8 & 87.7 & 77.8 & 89.4 \\ \hline \multirow{4}{*}{**Grayscale**} & Correct (Average) & 82.8 & 122.2 & 10.8 & 106.4 & 34.1 & 59.9 & 77.7 & \\  & Omitted (Average) & 14.0 & 25.2 & 9.5 & 13.5 & 0.0 & 8.9 & 37.1 & \\ Pre-Processed & Error (Average) & 19.2 & 16.2 & 0.7 & 10.1 & 2.9 & 4.2 & 15.1 & \\  & Accuracy (\%) & 71.4 & 74.5 & 51.4 & 81.8 & 92.2 & 82.1 & 59.8 & 73.3 \\ \hline \multirow{4}{*}{**Whitelist**} & Correct (Average) & 93.1 & 145 & 19.8 & 117.9 & 34.8 & 65.5 & 111.0 & \\  & Omitted (Average) & 4.0 & 0.9 & 0.1 & 3.4 & 0.0 & 0.8 & 1.9 & \\
**Applied** & Error (Average) & 19.5 & 17.7 & 1.1 & 8.7 & 2.2 & 6.7 & 17.0 & \\  & Accuracy (\%) & 80.3 & 88.4 & 94.3 & 90.7 & 94.1 & 89.7 & 85.4 & 89.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy performance of CIMI and its comparison with other algorithms. thickness was thicker than other PFTs, and the margins between letters is reasonable. The reason type 6 had lower accuracy than type 5 was because type 6 had more \"%Chg\" items than type 5. Evaluation based on PFT data obtained from patients in St. Mary's Hospital, Seoul, showed successful classification of PFT sheets. The CIMI accuracy comparison results were superior to those of other widely used original algorithms, with an accuracy of 95.3%. This was the result of numerous systematic comparison and combination test attempts to create the CIMI's algorithm accuracy to be optimized in the field of PFT. The limitation of this study was that CIMI was only applied to one medical institution for only one year (2019). Since medical data are prone to discrepancies among facilities, more data from other institutions and past/future years should be used and validate for future research. Moreover, the accuracy of CIMI was low for certain data types (especially in cases of type 1 and type 2); therefore, future investigation is needed to further enhance accuracy. CIMI is envisioned to be used as an impactful tool for future research on big data pulmonary disease analysis and future solutions to predict respiratory diseases. Conceptualization, T.M.K. and S.-J.L. ; Data curation, T.M.K. ; Formal analysis, T.M.K. and S.-J.L. ; Funding acquisition, I.-Y.C. ; Investigation, T.M.K. ; Methodology, T.M.K., D.-J.C.; Project administration, I.-Y.C. ; Resources H.Y.L. ; Software, T.M.K. ; Supervision, S.-J.L., I.-Y.C. and K.-H.Y. ; Validation C.I.Y. ; Visualization, T.M.K. ; Writing--original draft, T.M.K. ; Writing--review & editing, T.M.K. and S.-J.L. All authors have read and agree to the published version of the manuscript. This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1A5A2027588). The authors declare no conflict of interest.