## 4 Discussion  
In this study, we take advantage of deep learning to classify zebrafish larvae phenotypes from brightfield microscopic images. We aim to solve the classification problems especially for those phenotypes with fuzzy class features. Based on the same training and testing data, [PERSON] et al. had already developed a larvae phenotype classification method using the classical machine learning method [20]. Their method provides a state-of-the-art baseline for our DL-based approach. The experimental results of Figure 5 reveals that our deep-learning method outperforms the baseline method in terms of classification accuracy. Besides the accuracy improvement, the advantages of the deep-learning method also include the avoidance of designing hand-crafted image features. [PERSON] et al. used image edge and sub-window intensity distribution as the image features, but these features may not be optimal for classifying some difficult phenotypes like edema and hemostasis. Our deep-learning approach automatically learns the crucial image features for phenotype classification. The deep-learning approach not only relieves the algorithm developers from the burden of image \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **\# of Network** & **\# of Weight** & **Five-Phenotype** & **Eight-Phenotype** & **All-Phenotype** \\  & **Parameters** & **Layers** & **Combination** & **Combination** & **Combination** \\ \hline Xception & 22M & 16 & 0.86 & 0.92 & 0.92 \\ AlexNet & 60M & 8 & 0.80 & 0.75 & 0.70 \\ VGG16 & 138M & 16 & 0.85 & 0.87 & 0.84 \\ VGG19 & 144M & 19 & 0.88 & 0.87 & 0.85 \\ GoogLeNet & 5M & 22 & 0.85 & 0.86 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of classification accuracy between our Xception-based method with [PERSON]’s VGG16-based method [19] and our finetuning based on AlexNet [24], VGG19 [27], and GoogLeNet [28]. feature design but also automatically discovers the useful features which the human designers cannot conceive of. As a result, we observe dramatic accuracy improvement for the phenotypes with fuzzy class features, such as edema and hemostasis. To address the scarce training data problem, we performed augmentation to the training data. We augmented different phenotypes with different scales to compensate for the imbalanced training data. As Figure 5 reveals, this strategy decreased the inter-class accuracy differences compared to the baseline method using original training data. However, some phenotypes with too few original training samples (e.g., hemostasis) still have relatively lower accuracy than other phenotypes. This is because our augmentation process only implemented image shifting and rotation, its capacity to improve data variability is limited. For a future study, we will introduce more variations into the data augmentation step, such as non-linear image deformation and image contrast variations [29]. To cope with the problems of fuzzy inter-class differences and multi-label issues, we used the two-tier classification pipeline. Experiment results proved the effectiveness of the two-tier strategy. Accuracy improvements against the single-tier pipeline were observed for the difficult classes like edema, hemostasis and down-curved tail. The improvements for these phenotypes are 0.06, 0.03 and 0.09, respectively. It is worth mentioning that [PERSON] et al. also compared the two-tier and single-tier pipelines using their randomized tree classifier [20]. However, their two-tier pipeline only improved the accuracy of edema and down-curved tail by 0.01 and 0.03, respectively, which are much less than our improvements. For hemostasis, their two-tier pipeline even resulted in a 0.03 decrease in accuracy. It is obvious that the advantage of the two-tier strategy is more obvious for our method, thanks to the superior classification ability of the DL models. Currently, there are various types of DL models for image classification. We chose Xception as the classifier because of its small hyperparameter scale [25].