## 3 Results and Discussion
### Pattern Classification Results  
The performance of the pattern classification method was obtained at an image-level (i.e., in terms of the IIF images correctly classified). Table 7 shows the confusion matrix obtained for the 220 images analyzed. The classification results obtained in our experiments have been summarized and organized by the staining pattern class in Table 8.  
The Mean Class Accuracy (MCA) obtained was equal to 73.1%, with a maximum per-class accuracy of 96.2%, for the centromere patterns, and a minimum per-class accuracy of 43.5%, for the nuclear membrane. The accuracy obtained was equal to 70.9%. Fluorescent pattern performance was evaluated not only at the image-level but also at the well-level (in terms of wells correctly classified), obtaining, in this case, an increase in performance, in particular: MCA = 75.6% and Accuracy = 73.6%.  
Table 9 shows the values of accuracy, MCA, and the number of images on which the analysis was carried out, for the various CAD systems proposed in recent literature. In order to have an effective performance comparison, a further analysis was carried out, training and testing (with the leave-on-specimen-out method) on the I3 Asel database originates from the First Workshop on Pattern Recognition Techniques for IIF images (I3A) [15]. The result obtained is shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \cline{3-8} \multirow{4}{*}{Actual Class} & & **HO** & **SP** & **CE** & **NU** & **DOT** & **ME** & **TOT** \\ \cline{3-8}  & HO & 19 & 0 & 0 & 2 & 0 & 0 & 21 \\  & SP & 3 & 38 & 0 & 0 & 0 & 1 & 42 \\  & CE & 1 & 0 & 25 & 0 & 0 & 0 & 26 \\  & NU & 4 & 10 & 8 & 34 & 4 & 2 & 62 \\  & DOT & 0 & 6 & 2 & 5 & 30 & 3 & 46 \\  & ME & 3 & 9 & 1 & 0 & 0 & 10 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The confusion matrix for our method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Class**} & **Accuracy** \\ \hline homogenous & 90.5\% \\ speckled & 90.5\% \\ centromere & 96.2\% \\ nucleolar & 54.8\% \\ nuclear dots & 65.2\% \\ nuclear membrane & 43.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Per-class accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Images Dataset** & **Accuracy** & **MCA** \\ \hline [PERSON] et al. [22] & 71 (I3A) & 73.4\% & – \\ [PERSON] et al. [24] & 1006 & 79.3\% & 79.4\% \\ [PERSON] et al. [23] & 17 & 99.7\% \\ Our method & 71 (I3A) & 75,1\% & 78,6\% \\ Our method & 220 (AIDA) & 70.9\% & 73.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of performance between the adopted method and previous investigations.  
The overall result of accuracy, obtained by analyzing the AIDA database, was negatively affected by the performance obtained on the nuclear membrane pattern; this type of pattern was not analyzed in any of the studies that were used for comparison. If the pattern nuclear membrane is not considered, the obtained performances at the image-level would be: MCA = 79.4% and Accuracy = 74.1%.  
The performances obtained on the I3A database, can easily be compared with the method proposed by [PERSON] [22], using the same database.  
It should be noted that, when the analysis was conducted on a few images (for example in the work of [PERSON] et al. [23]), and especially if in the development the authors tune the method on the few data available to them, it is possible obtain overestimated performance values.  
It is clear that in a CAD system, the classification phase is dependent on the segmentation phase; as a primary effect, the segmentation inaccuracies have differences in the values of the characteristics, and consequently, errors in the classification phase. Since the classification has not been obtained from manually segmented regions, but instead, the system is totally automatic, this consequently leads to a significant decrease in the classification performance.