## 3 Experiments and Results
### Result Comparisons  
**Experiment 1**: Comparison to the traditional method [25; 27]  
We first conduct our test on the Duke datasets. To compare our method with that in Ref. [25] and Ref. [27] fairly, we also use leave-three-out cross-validation for 45 times as done in Refs. [25; 27]. In each experiment, 42 volumes are chosen as training set and the other 3 volumes (one volume from each class) as testing set so that each of the 45 SD-OCT volumes can be classified once. Since a volume contains many OCT scans of a specific person, majority voting of all predicted labels for each subjectis treated as class (AMD, DME, or NOR) for the corresponding subject [25]. We do not artificially exclude the normal tissue slices of AMD or DME as mentioned in the Ref. [25]. The cross-validation results can be seen in Table 2. As can be seen from Table 2, 100% of 45 OCT volumes are correctly classified with our method which invariably performs better than the HOG-SVM [25] (95.56%) and ScSPM [27] (97.78%). **Experiment 2**: Comparison to transfer learning-based method [31]  
Recently, some methods based on transfer learning have been proposed to classify the retinal OCT B-scans. According to Ref. [31], The dataset is divided into 15 folds, with each fold containing three subjects (one from each class). However, different from the setting in experiment 1, each validation involves eight folds (24 volumes) for training and the rest seven folds (21 volumes) for testing. Folds are sequentially chosen instead of randomly. The mean of decision pooling across all validations for AMD, DME, and NOR are reported in Table 3. The sensitivity of the HOG-SVM and deep CNN are quoted from Ref. [36]. As can be seen, the proposed method delivers better performance than HOG-SVM and deep CNN in total. **Experiment 3**: Comparisons on classification performance of retinal OCT B-scans  
To evaluate the classification performance of our method on retinal OCT B-scans, we conduct more experiments on the Beijing clinic dataset with different proportion. To obtain reliable results, we repeat the experimental process 10 times with different randomly selected images in the clinic dataset for training the rest for testing. Firstly, we choose a quarter of the images of each class (140 AMD, 140 DME, and 140 NOR) for training and the rest (420 AMD, 420 DME, and 420 NOR) for testing. Then we choose half of the images of each class (280 AMD, 280 DME, and 280 NOR) and the rest (280 AMD, 280 DME, and 280 NOR) for testing. Tables 4 and 5 detail the classification results of each category. The overall classification results are tabulated in Table 6 (the best results among different methods are labeled in bold). From the results we can see that the proposed method performs better than ScSPM [27] and IBDL [34]. In particular, IBDL uses more than 100,000 OCT B-scans as training set with accuracy, sensitivity, and specificity above 95% according to Ref. [34]. However, the classification performance of IBDL method running on our dataset is not very good, indicating that it is not suitable for small-scale dataset. In conclusion, the proposed method has better classification performance on small-scale datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **HOG-SVM [25]** & **ScSPM [27]** & **Ours** \\ \hline AMD & 15/15 = 100.00\% & 15/15 = 100.00\% & 15/15 = 100.00\% \\ DME & 15/15 = 100.00\% & 15/15 = 100.00\% & 15/15 = 100.00\% \\ NOR & 13/15 = 86.67\% & 14/15 = 93.33\% & 15/15 = 100.00\% \\ Overall & 43/45 = 95.56\% & 44/45 = 97.78\% & 45/45 = 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fraction of volumes correctly classified with the three methods on the Duke dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **HOG-SVM [25]** & **Deep CNN [31]** & **Ours** \\ \hline AMD & 89 & 89 & 89 \\ DME & 83 & 86 & 92 \\ NOR & 90 & 99 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fraction of volumes correctly classified with the three methods on the Duke dataset.