## 4 Experiments and Results
### Classification Performance Comparison with Other Machine Learning Methods  
To verify the effectiveness of the LCCDNN model for the neuronal morphological classification problem, we compared the following mainstream machine learning approaches: logistic regression based on generalized linearity [8; 36], naive Bayesian classifier based on prior probability estimation [10; 12], decision tree (DT) based on information entropy [37], k-nearest neighbor (KNN) [38], support vector machine based on geometric distance [9], and long short-term memory (LSTM) network model [39]. In addition, the deep neural network models such as FCDNN and RCDNN are also used for classification performance comparison. These learning methods are implemented using SKLEARN [32], and the parameters of the learning methods have been tested and fine-tuned based on the recommended values in the simulation experiments. For LR, the 'liblinear' solver was used, which supports the L2 penalty, the tolerance for stopping criteria was 1e-4, the inverse of regularization strength was 1.0, the maximum number of iterations was 100, and other parameters were the default. For NB, the variance smoothing was 1e-09 and the other parameters were the default. For DT, the criteria = 'gini' and splitter = 'best', the minimum number of samples required to split an internal node was two, the minimum number of samples required to be at a leaf node was one, the threshold for early stopping in tree growth min_impurity_split was 1e-7, and other parameters were the default. For KNN, the number of neighbors was five, the metric was'minkowski' with the power parameter of two, and the leaf size was 30. For SVM, the kernel was linear function, the penalty = 'L2' and loss ='squared_hinge', the penalty parameter C of the error term was 1.0, and other parameters were the default.  
Figure 17: Confusion matrix of neuronal classification by LCCDNNs. (**a**) C. elegans dataset, (**b**) zebrafish dataset.  
Figure 18: Confusion matrices of neuronal classification by FCCDNNs. (**a**) C. elegans dataset, (**b**) zebrafish dataset.  
Table 3 compares the classification accuracy of the LCCDNN and FCCDNN learning models against existing classifiers for the C. elegans and zebrafish datasets. The FCDNN model had the best effect on the C. elegans dataset with a layer parameter of 30 and a classification accuracy of 85.9% for the test set. When the layer number was 20, the FCDNN model obtained the best classification accuracy of 75.12% on the zebrafish dataset. For the RCDNN learning model, on the C. elegans dataset, the use of one building block and 10 intermediate layers per building block showed the best classification accuracy on the training set and the test set of 97.71% and 85.9%, respectively; on the zebrafish dataset, using four building blocks and 10 intermediate layers, the best classification accuracy on the training set and the test set was 93.79% and 78.14%, respectively. The LSTM network model has two layers and two step sizes, and the number of neurons in each layer was 50. The LSTM's classification accuracy on the two neuron datasets was 68.46% and 70.47% for the test set.  
For the two datasets, the classification accuracies of the LCCDNN model were comparable to that of the other learning methods. When LCCDNN contained three building blocks and 10 intermediate layers per building block, the classification accuracy on the training set and the test set of the C. elegans dataset was 98.03% and 87.44%, respectively. When the number of building blocks and the number of intermediate layers were both five, the classification accuracy of the LCCDNN on the training set and the test set of zebrafish dataset was 98.16% and 81.16%, respectively. It can be seen that the classification accuracy of the LCCDNN model was higher than that of the other approaches such as the traditional classifiers LR, NB, DT, KNN, and SVM, and the neural network models LSTM, FCDNN, RCDNN, and FCCDNN.