## 4 Discussion and Conclusions  
On analyzing the results, in general terms and considering the mean results reported in Table 2, when using the standard evaluation technique, the prediction over C-scan volumes was slightly better than the prediction over individual B-scan images. This impression is confirmed by the SRD analysis (Figure 11), where smaller values were obtained for C-scan images analysis. This result makes sense, since when evaluating the lesion volumetrically (C-scan) considering the mean prediction of all the B-scan images contained in the C-scan, there was less probability of a bad prediction. If the volume contains some individual B-scans with poor information representing the class sample, the (expected) bad predictions do not have great influence on the final diagnosis. In any case, the small differences on the prediction metrics suggest the high quality of the database used in this study, as shown in the detailed results for each fold provided in Table 2.  
It can also be observed that the TTA evaluation technique slightly benefitted the prediction over individual B-scan images in terms of sensitivity and specificity, but not the C-scan volume prediction. However, these results make sense for two reasons: the data preparation strategy and the volumetric evaluation of the lesion. On the one hand, due to the nature of the images, no geometrical transformations were applied for data augmentation, as described in the data preparation section, but ROIs at different location of the image were extracted. Depending on the location of the extracted ROIs, the clinical features can be more or less representative of the lesion, affecting the corresponding prediction. When TTA was applied, different ROIs from the B-scan were extracted, allowing analysis of the overall sample in width, and hence a better prediction was obtained. This is particularly beneficial in the case of large wide B-scan images, as it allows analyzing the different parts of the tissue/lesion in detail. Considering this, and although no improvement was observed on the C-scan evaluation, the TTA strategy was preferred during the evaluation, since in this way, the intrinsic clinical variability of the lesions was captured and hence the model prediction was more robust. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Type** & **Evaluation** & **BAC** & **Sensitivity** & **Specificity** & **PPV** & **NPV** \\ \hline B-scan & Standard & 0.8806 \(\pm\) 0.0748 & 0.9635 \(\pm\) 0.0148 & 0.7978 \(\pm\) 0.1431 & 0.9268 \(\pm\) 0.0498 & 0.8914 \(\pm\) 0.0415 \\  & TTA & 0.8895 \(\pm\) 0.0792 & 0.8094 \(\pm\) 0.1524 & 0.8094 \(\pm\) 0.1524 & 0.9305 \(\pm\) 0.0526 & 0.9093 \(\pm\) 0.0400 \\ \hline C-scan & Standard & 0.8857 \(\pm\) 0.1143 & 0.7893 \(\pm\) 0.2180 & 0.7893 \(\pm\) 0.2180 & 0.9221 \(\pm\) 0.0735 & 0.9432 \(\pm\) 0.0687 \\  & TTA & 0.8843 \(\pm\) 0.1068 & 0.7865 \(\pm\) 0.2050 & 0.7865 \(\pm\) 0.2050 & 0.9212 \(\pm\) 0.0693 & 0.9472 \(\pm\) 0.0614 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of results by the network for the different imaging modalities (B-scan vs. C-scan), applying different evaluation techniques (standard vs. test time augmentation (TTA)) and resampling imbalance strategy. Note that the numbers report “mean \(\pm\) std” values. Interpretation of new imaging techniques, such as OCT, can be complicated at the beginning and prevent their adoption in clinical practice. However, advanced image processing techniques, such as deep learning, can be used to facilitate automatic image analysis or diagnosis and the development of optical biopsy. A previous work [46] proposed using a pattern recognition network that requires prior manual annotation of the dataset and diagnosis depends on whether the expected pattern is found on the image. Alternatively, this work proposes using a classification strategy, which can help in the identification of subtle clinical characteristics on the images and is not biased by dataset annotations.