Tomogram Analysis

The aim of our experiment was to successfully classify given data into the ten different categories of protein macro- molecules. Convolutional Neural Network (CNN) models are a widely used classification method. It can achieve significantlyhigher classification accuracy than other rotation invariant fea- ture based methods. Additionally, CNN models scale linearly with respect to the number of inputs, given fixed subtomogram size and class number. Thus, we adapted a simple CNN architecture which achieved a high accuracy for classification tasks. The CNN architecture consists of two blocks. Each block is com- prised of two convolution layers and one max pool layer. The first block uses 32 filters on each convolution layer, whereas the second uses 64 filters on each of the two convolution layers. All convolution layers use ‘ReLU’ activations. These blocks are followed by two fully connected layers of 512 neurons respectively. Finally, the output layer of 10 units uses a ‘Softmax’ activation.

We separated each of the three sets of subtomograms into training and testing sets with ratio 4:1. We used 80% of our data for model fitting and the rest for validation. In our experiments, we were able to achieve relatively high validation accuracy for all SNR levels. Our results show that lower SNR ratios lead to lower classification accuracy. We see that the CNN model has achieved the highest accuracy for data with infinite SNR, and the lowest for SNR 0.03.