# Classification of OCT Images of the Human Eye Using Mobile Devices
## 5 Conclusions  
Diagnostic solutions based on artificial intelligence can support doctors in interpreting medical data (for example, assessing and detecting pathological changes in the image) and at the same time be a digital second opinion. Of course, the final decision is always made by the doctor. Modern smartphones have sufficient computing power to implement software using deep neural network architectures.  
The presented solution, available in open source form, can be further developed. The user interface in the B-scan classification process shows decision probabilities for three types of DNN architects. This type of solution is helpful in further evaluation of the selection of solutions. The achieved values of the correctness metrics depend on the type and size of the training data. However, it should be noted that the simplified architectures operating on smartphones using TensorFlow Lite do not cause changes in the values of such metrics.  
Conceptualization, T.M.; methodology, A.S. and T.M.; software, R.C., N.B. and M.D.; validation, T.M. and A.S.; formal analysis, T.M. and A.S.; investigation, T.M. and A.S.; resources, T.M. and A.S.; data curation, A.S.; writing--original draft preparation, T.M. and A.S.; writing--review and editing, T.M., A.S., R.C., N.B. and M.D.; visualization, R.C., N.B. and M.D.; supervision, A.S. and T.M.; funding acquisition, T.M. All authors have read and agreed to the published version of the manuscript.  
This work was prepared within Poznan University of Technology project number 0211/S-BAD/0225.  
Not applicable.  
**Data Availability Statement:** The Python code supporting the reported results can be found in the GitHub repository under the link [[https://github.com/[PERSON]/OCT-Classifier](https://github.com/[PERSON]/OCT-Classifier)]([https://github.com/[PERSON]/OCT-Classifier](https://github.com/[PERSON]/OCT-Classifier)) (accessed on 31 January 2025). The OCT2023 dataset utilized in the study is available on request.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Phone Model**} & \multirow{2}{*}{**Image Origin**} & \multicolumn{2}{c}{**VGG16**} & \multicolumn{2}{c}{**JAN + MobileNetV2**} & \multicolumn{2}{c}{**OpticNet-71**} \\  & & **ACC** & \begin{tabular}{c} **Time** \\ **[ms]** \\ \end{tabular} & \begin{tabular}{c} **ACC** \\ \end{tabular} & \begin{tabular}{c} **Time** \\ **[ms]** \\ \end{tabular} & \
\begin{tabular}{c} **ACC** \\ \end{tabular} &
\begin{tabular}{c} **Time** \\ **[ms]** \\ \end{tabular} \\ \hline \multirow{3}{*}{Motorola Moto G7 Play} & Phone gallery & 80\% & 5099.4 & 60\% & 412.6 & 80\% & 3187.2 \\  & Matte monitor photo & 100\% & 5221.2 & 100\% & 405.4 & 60\% & 3220.8 \\  & Projection photo & 80\% & 4970.4 & 60\% & 350.8 & 40\% & 3264.0 \\ \hline \multirow{3}{*}{Samsung Galaxy S8} & Phone gallery & 80\% & 2980.8 & 60\% & 365.0 & 80\% & 1515.2 \\  & Matte monitor photo & 80\% & 2515.6 & 100\% & 330.2 & 60\% & 1493.4 \\  & Projection photo & 60\% & 2407.0 & 80\% & 275.4 & 40\% & 1480.8 \\ \hline \multirow{3}{*}{Oneplus 8 Pro} & Phone gallery & 80\% & 931.4 & 60\% & 90.8 & 80\% & 570.6 \\  & Matte monitor photo & 60\% & 916.2 & 80\% & 88.6 & 20\% & 579.2 \\ \cline{1-1}  & Projection photo & 80\% & 961.2 & 80\% & 92.2 & 40\% & 589.4 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Prediction times and accuracy for tested mobile phones.  
**Acknowledgments:** We would like to thank a team of ophthalmology experts from the Department of Ophthalmology, Chair of Ophthalmology and Optometry, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences for validation of the OCT B-scan dataset used for the experiments.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.