## 3 Results and Discussion  
We used single eardrum images to train and test the system for the transfer learning part of the training with classification accuracy as a measure for each training fold. We retrained the transfer learning for extracting the lookup table values twice: before and after adding normal-abnormal image pairs. Because the number of 'normal-abnormal' pair images was limited, we started with 'normal-normal' and 'abnormal-abnormal' pair images and with the classification categories of normal or abnormal. Subsequently, we performed the experiments adding 'normal-abnormal' eardrum pair images and compared them. Table 2 shows these two experimental results for these cases. Before adding normal-abnormal pairs' eardrum images, training, validation, and testing accuracies were 88.8% \(\pm\) 3.3%, 86.7% \(\pm\) 6.7%, and 83.3% \(\pm\) 3.3%, respectively, as seen in Table 2. However, adding normal-abnormal cases decreased the accuracies for to 83.6% \(\pm\) 6.3%, 78.4% \(\pm\) 6.8%, and 78.7 \(\pm\) 0.1%. This training step was used just for creating a lookup table and extracting lookup table features. After creating the lookup table with transfer learning, we experimentally tested the lookup table-based feature extraction and handcrafted feature extraction (see Methodology Section). Lookup table based feature extraction was the first step of the feature extraction phase. The handcrafted features were registration errors, the number of counts in bins of the histogram of L*a*b*, mean, and other statistical measurements (standard deviation, skewness, and kurtosis), and these were concatenated in each step, and the system was tested after each concatenation. Figure 4 presents the system accuracies for normal-normal (N-N), abnormal-abnormal (A-A), and all pairs (N-N, A-A, N-A, and A-N). After adding N-A, A-N pair images, the accuracies decreased. Before the N-A pairs added to the dataset, the lookup table based feature extraction accuracy result was the highest accuracy with 85.1% and 7.6% low standard deviation. After adding A-N pairs, the highest accuracy result was 85.8% \(\pm\) 6.4% \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Before Normal-Abnormal Cases Added**} & \multicolumn{3}{c}{**After Normal-Abnormal Cases Added**} \\  & **Training** & **Validation** & **Testing** & **Training** & **Validation** & **Testing** \\ \hline Fold 1 & 89.1\% & 86.7\% & 80.3\% & 76.3\% & 82.4\% & 78.8\% \\ Fold 2 & 85.4\% & 93.3\% & 82.9\% & 86.8\% & 70.6\% & 78.8\% \\ Fold 3 & 92.0\% & 80.0\% & 86.8\% & 87.6\% & 82.4\% & 78.6\% \\ \hline Average & 88.8\% & 86.7\% & 83.3\% & 83.6\% & 78.4\% & 78.7\% \\ Standard Deviation & 3.3\% & 6.7\% & 3.3\% & 6.3\% & 6.8\% & 0.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Transfer learning classification accuracies for single images as normal or abnormal. for concatenated registration error and histogram of L*a*b* features to the lookup table based features. When the paired image classification accuracies are compared with those of the single images, we observe some improvements from 83.3% (SD \(\pm\) 3.3%) to 85.1% (SD \(\pm\) 7.6%) (N-N and A-A pairs) and from 78.8% (SD \(\pm\) 0.1%) to 85.8 (SD \(\pm\) 6.4%) (N-N, A-A, N-A, and A-N pairs). Unfortunately, most of the improvements are not statistically significant according to the t-test between the classification results of transfer learning and each tested pair features. For the same category pair (i.e., N-N and A-A) images, the average \(p\)-value is 0.80, while, for all category pair (N-N, A-A, N-A, and A-N) images, the average \(p\)-value is 0.16. While the \(p\)-values decreased, they were not statistically significant (<0.05). However, the \(p\)-value of the t-test between the classification results of transfer learning and all features (except for the mean value of the L*a*b* color space) is 0.0004, which is statistically significant.