## 3 Experiments and Results
### Performance of the Sub-Networks on Small-Scale Dataset  
The small-scale retinal OCT dataset is obtained from clinics in Beijing hospital, using CIRRUS TM (Heidelberg Engineering Inc., Heidelberg, Germany). This dataset consists of 560 AMD, 560 DME and 560 normal (NOR) images. All the SD-OCT images are read and assessed by the trained graders. We first preprocessed these images using the preprocessing method in [14] which is mainly divided into three stages. (1) In the perceiving stage, the method detects the overall morphology of a retina. Firstly, the sparsity-based block matching and 3-D-filtering (BM3D) denosing method [34] is used to reduce noises of the OCT image, then the binarization, median filtering, morphological closing and morphological opening methods are used to obtain the subject of the image. (2) In the fitting stage, the method automatically chooses the set of data points and a fitting method (linear fitting or second-order polynomial fitting). (3) In the normalizing stage, the method normalizes the retinas by aligning them to a relatively unified morphology and crops the images to trim out insignificant space. Some examples are shown in Figure 5.  
Then the preprocessed dataset is divided into training set of 840 images (280 AMD, 280 DME and 280 NOR) and test set of 840 images (280 AMD, 280 DME and 280 NOR). We finetuned all the layers of the sub-networks using the same batch size, Adam optimizer and input size as in large-scale experiment. The only difference is that we trained for 30 epochs in the small-scale dataset instead of 20. Each experiment conducts 10 times. The mean \(\pm\) standard deviation of the value of accuracy, sensitivity and specificity were calculated. We compared our algorithm with some state-of-the-art work. The algorithm of spatial pyramid matching using sparse coding (ScSPM) [14] utilizes techniques such as SIFT, SC, K-SVD, multi-scale max pooling and linear SVM. The algorithm of deep learning-based CNN (DL-based CNN) [35] removes the last several layers from the pre-trained Inception-v3 and regards the remaining part as a fixed feature extractor. Then the features are used as input of a CNN designed to learn the feature space shifts. Results in Table 5 show that all the sub-networks of Inception-v3, ResNet50 and DenseNet121 outperform ScSPM [14], DL-based CNN [35] and IBDL [9]. In the case of Inception-v3, \"mixed6\" reaches the highest accuracy of 99.67% and the minimal standard deviation of 0.08%. Note that method of finetuning complete inception-v3 (\"mixed10\") only achieves accuracy of 99.21% and standard deviation of 0.41%. It shows improvement both in accuracy and stability of the sub-network removing deep layers, which means that the removal of deep convolution layer with a high-level feature in pre-trained Inception-v3 enhances the process of transfer learning. Figure 5: Beijing clinical small-scale dataset. First row shows original AMD, DME and normal optical coherence tomography (OCT) images. Second row shows AMD, DME and normal OCT images after pre-processing. Results of ResNet50 demonstrate similar conclusion. \"Ac37\" (99.49 \(\pm\) 0.08%) outperforms all ResNet50 sub-networks discussed in the article, including complete ResNet50 (\"mixed10\" with 99.09 \(\pm\) 0.25%). Let us claim that \"mixed6\" and \"Ac37\" perform better than their corresponding entire network. In inferential statistics, the null hypothesis is that the mean accuracy of sub-network is the same as that of entire network. We measure the accuracy of ten repeated experiments and conduct one-way analysis of variance with \(\alpha\) = 0.05. As Table 6 shows, P-value is 7.73 \(\times\) 10\({}^{-3}\) in the Inception-v3 group and 6.94 \(\times\) 10\({}^{-4}\) in the ResNet group, both far less than \(\alpha\); F value is 8.984 in the Inception-v3 group and 16.690 in the ResNet group, both greater than the critical value 4.414. Results mean that we reject the null hypothesis. The analysis demonstrates significant difference of accuracy between the sub-network and entire network.