## 4 Conclusions  
In this study, we propose a system for classifying pair eardrum images together as 'normal-normal', 'abnormal-abnormal', and 'abnormal-normal'. To the best of our knowledge, this is the first study that classifies a pair of eardrum images of the same patient together. We used two feature extraction methods to classify the pair of images: deep learning-based and handcrafted, and combined the resulting features of two sides of eardrum images to classify the pair of images together. Subsequently, we analyzed the results of one side of eardrum images and pair eardrum images with and without 'normal-abnormal' and 'abnormal-normal' cases.  
We also compared the results after extracting each group of features of the paired images. According to the experimental results, the highest accuracy was 85.8% (\(\pm\)6.4%) for all types of pair image classification. The features of concatenated registration error and histogram of L*a*b* features. However, the only statistically significant result of the difference between single side eardrum image classification with transfer learning was due to all of the extracted and concatenated features (without the feature of the mean of L*a*b* color space) with 83.5% (\(\pm\)0.2%) accuracy. Other experiments did not create any statistically significant difference. Still, at least one statistically significant result is promising with all concatenated features, except the mean of L*a*b* color space features.  
One of the study's limitations is the small number of A-N (abnormal-normal) paired images, and the abnormal class only consists of otitis media effusion and tympanostomy tube categories. In addition to this, 63.3% (19/30) of the existing A-N pair images were subtle as assessed by a specialist. Future studies will include a larger number of pair images for each category of eardrum pairs. We also observed that transfer learning based lookup table values for the same category pairs could be easily classified according to differently labeled pair images. Therefore, we can use the lookup table values to select subtle images and automatically eliminate them from the training dataset for future studies.  
Conceptualization, S.C., A.C.M., and M.N.G.; methodology, S.C., and M.N.G.; software, S.C.; validation, S.C., and M.N.G.; formal analysis, S.C., and M.N.G.; investigation, S.C.; resources, A.C.M., T.T., G.E., C.E., N.T.-S., and M.N.G.; data curation, A.C.M., T.T., G.E., C.E., and N.T.-S.; Writing--Original draft preparation, S.C.; Writing--Review and editing, S.C., A.C.M., and M.N.G.; visualization, S.C.; supervision, A.C.M., and M.N.G.; project administration, A.C.M., and M.N.G.; funding acquisition, A.C.M., and M.N.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Award R21 DC016972 (PIs: [PERSON], [PERSON]) from National Institute on Deafness and Other Communication Disorders. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Deafness and Other Communication Disorders or the National Institutes of Health.  
**Institutional Review Board Statement:** The images used in this study were captured at Ear, Nose, and Throat (ENT) and primary care clinics from both adult and pediatric patients at the Ohio State University (OSU) and Nationwide Children's Hospital (NCH) in Columbus, Ohio, US with the IRB approval (Study Number: 2016H0011). Furthermore, all the samples were fully anonymized by the rules set by the Ohio State University, Institutional Review Board.  
**Data Availability Statement:** The data that support the findings of this study are openly available in Zenodo at [[https://doi.org/10.5281/zenodo.4404755](https://doi.org/10.5281/zenodo.4404755)]([https://doi.org/10.5281/zenodo.4404755](https://doi.org/10.5281/zenodo.4404755)).  
**Conflicts of Interest:** Computing interest: [PERSON] and [PERSON] are directors of Otologic Technologies, Inc.