## 3 Results
ResNet50 [36] is the 50-layer deep version of ResNet (Residual neural Network), an exotic architecture that is based on \"network-in-network\" micro-architectures. Due to global average pooling, instead of fully connected layers, the size of ResNet50 is significantly smaller than VGG16. Finally, Inception V3 [37] is Inception's third instalment, and is 48-layers deep. Inception V3 incorporated RMSProp optimizer and \(7\times 7\) convolution in the \(1\times 1\), \(3\times 3\) and \(5\times 5\) convolutions which were already presented within the same module of the network. For our comparison, we use them together with their weights, and retrain only the last layers for our specific dataset, something that is called transfer learning. Thus, each algorithm is re-trained/fine-tuned on our dataset, with the same preprocessing functions, in order to compare only the models' performance. Additionally, the training times of each algorithm have been measured with the %%time function of Python under a Jupyter Notebook. This function affects only the cell where it is called from, and was set to measure the execution time of only the training of the model and none of the preprocessing or the processing of the predictions. The results are shown in Table 9. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Loss**} \\ \cline{2-5}  & _Validation_ & _Testing_ & _Validation_ & _Testing_ \\ \hline _Grayscale_ & 92.869 & 92.469 & 0.192 & 0.197 \\ _Fast Fourier_ & 93.153 & 92.938 & 0.198 & 0.176 \\ _RGB_ & 96.847 & 95.969 & 0.086 & 0.111 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy and loss for the validation and testing sets, for the three preprocessing algorithms, on images with background information. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Loss**} \\ \cline{2-5}  & _Validation_ & _Testing_ & _Validation_ & _Testing_ \\ \hline _Grayscale_ & 95.710 & 95.469 & 0.116 & 0.130 \\ _Fast Fourier_ & 96.591 & 96.531 & 0.108 & 0.105 \\ _RGB_ & 99.006 & 98.719 & 0.031 & 0.049 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy and loss for the validation and testing sets, for the three preprocessing algorithms, on images without background information. We notice that the proposed CNN architecture has performed better than the rest of the state-of-the-art architectures, except Inception V3, which achieved better accuracy both in the validation dataset, as well as in the testing dataset. A significant difference between the proposed architecture and the others, is that the proposed CNN is significantly shallower compared to the others, therefore it can be trained a lot faster on the same dataset. It is clear that the proposed network achieves a similar accuracy to the state-of-the-art Inception V3 network, in less time due to its shallower architecture. The tradeoff is in favor of the proposed network, since the difference in time is significant, while the accuracy difference is minimum. Given that in this particular problem the useful features are not so complex, the proposed CNN performs satisfactorily.