# Deep CNN for IIF Images Classification in Autoimmune Diagnostics
## 3 Results  
The performance of the method proposed here was obtained both at cell-level and at image-level. The best confusion matrix, obtained with the LOSO procedure on the 13,596 cell images present in the Task-1 database, is presented in Table 2.  
The results contained in Table 2 have been summarized in Table 3 in order to highlight the per-class accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Homogenous** & **Speckled** & **Nucleolar** & **Centromere** & **Golgi** & **Nuclear Membrane** \\ \hline Homogenous & 2194 & 215 & 7 & 21 & 36 & 21 \\ Speckled & 261 & 2328 & 91 & 57 & 83 & 11 \\ Nucleolar & 80 & 312 & 2077 & 45 & 66 & 18 \\ Centromere & 48 & 379 & 114 & 2144 & 53 & 3 \\ Golgi & 50 & 17 & 39 & 0 & 604 & 14 \\ Nuclear Membrane & 256 & 43 & 19 & 2 & 95 & 1793 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of our method at cell-level.  
An overall accuracy of 81.93% and an MCA of 82.16%, at cell-level was achieved.  
A performance analysis was performed to evaluate the classification power contained in the region immediately adjacent to the cell, using the boundary box containing the generic ROI rather than the segmentation mask. In this configuration the performances were slightly decreased and in particular the following were obtained: Accuracy = 80.3%, MCA = 79.9%.  
The prevalence of patterns at the cell-level was analyzed and, for classification at the image-level, the generic image was associated to the most present pattern. Table 4 shows the confusion matrix at the image-level.  
The mean class accuracy obtained was equal to 93.8%, while the accuracy achieved was equal to 96.4%. In the same configuration, the performance results without using the KNN classifier were: Accuracy = 94.0%, MCA = 91.5%.  
Regarding the analysis of the features and the use of CNN, the best configuration of the method, which allowed us to obtain the confusion matrix of Table 4, made use of the 4096 features of the Fc7 layer. Table 5 shows the details of the performances obtained when the pre-trained network layer changes.  
The best performances, at image-level, obtained without the phase of data augmentation were: Accuracy = 94.0%, MCA = 91.67%. For all the analyzed layers, when using the data augmentation there has always been an increase in performance; the magnitude of the increase was between 2% and 4%.  
In order to have a clear comparison of the performances, Table 6 reports the values of accuracy, MCA and the training method used, for various CAD systems proposed in the recent literature and using the database I3A Task-1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Homogenous** & **Speckled** & **Nucleolar** & **Centromere** & **Golgi** & **Nuclear Membrane** \\ \hline Homogenous & 16 & 0 & 0 & 0 & 0 & 0 \\ Speckled & 0 & 15 & 0 & 1 & 0 & 0 \\ Nuclear & 0 & 0 & 16 & 0 & 0 & 0 \\ Centromere & 0 & 1 & 0 & 15 & 0 & 0 \\ Golgi & 0 & 0 & 0 & 0 & 3 & 1 \\ Nuclear Membrane & 0 & 0 & 0 & 0 & 0 & 15 \\ \hline \end{tabular}
\end{table}
Table 4: Confusion matrix of our method at image-level.  
\begin{table}
\begin{tabular}{c c} \hline
**Class** & **Accuracy** \\ \hline Homogenous & 87.97\% \\ Speckled & 82.23\% \\ Nucleolar & 79.94\% \\ Centromere & 78.21\% \\ Golgi & 83.42\% \\ Nuclear membrane & 81.20\% \\ \hline \end{tabular}
\end{table}
Table 3: Per-class accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline
**Layer** & **Accuracy** & **MCA** \\ \hline Fc6 & 93.99\% & 91.67\% \\ Fc7 & **96.37\%** & **93.75\%** \\ Fc8 & 93.93\% & 91.67\% \\ Relu6 & 92.80\% & 90.63\% \\ Relu7 & 92.74\% & 90.63\% \\ Drop6 & 92.80\% & 90.63\% \\ Drop7 & 92.74\% & 90.63\% \\ \hline \end{tabular}
\end{table}
Table 5: Performance analysis obtained using different layers of the pre-trained network as feature extractors.