# Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours
## 3 Results
### Features Based on the Dct and the Dft. a Comparative Study
#### 3.1.2 LOO-CV Tests
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Multi-Class Classification Accuracy (LOO-CV 1000 Iterations)**} \\ \hline
**Hidden** & **DCT-5** & & & & **DFT-5** & & \\
**Nodes** & **mean** & **std** & **max** & **min** & **mean** & **std** & **max** & **min** \\ \hline
5 & 72.66 & 0.024 & 79.63 & 64.82 & 56.78 & 0.035 & 66.67 & 44.44 \\
10 & 72.66 & 0.024 & 79.63 & 64.82 & 72.29 & 0.029 & 81.48 & 63.89 \\
15 & 76.66 & 0.023 & 84.26 & 68.52 & 77.78 & 0.024 & 85.19 & 69.44 \\
20 & 78.62 & 0.022 & 85.19 & 72.22 & 80.52 & 0.024 & 88.89 & 72.22 \\
25 & 79.84 & 0.023 & 87.04 & 72.22 & 82.04 & 0.021 & 87.96 & 74.07 \\
30 & 80.42 & 0.023 & 87.04 & 72.22 & **82.59** & **0.021** & **88.89** & **75.00** \\
35 & **80.64** & **0.022** & **87.04** & **73.19** & 82.49 & 0.022 & 87.96 & 75.00 \\
40 & 80.54 & 0.023 & 87.96 & 73.19 & 81.94 & 0.022 & 88.89 & 74.07 \\
45 & 80.00 & 0.024 & 87.04 & 72.22 & 81.18 & 0.029 & 87.04 & 74.07 \\
50 & 79.38 & 0.025 & 87.04 & 71.30 & 79.79 & 0.024 & 86.11 & 72.22 \\
55 & 78.18 & 0.025 & 87.04 & 69.44 & 78.15 & 0.025 & 85.19 & 68.52 \\
60 & 76.71 & 0.026 & 85.18 & 68.52 & 76.26 & 0.027 & 85.19 & 68.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The multi-class classification problem. Comparative summary of the network accuracy obtained for the best combinations of the DCT (five features) and the DFT (five features) depending on the hidden nodes used. For each feature set, there is an optimum number of hidden nodes providing the best results. In the table the tests accuracy results are highlighted in bold. The LOO-CV was performed and the experiments were repeated 1000 times. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Multi-Class Classification Accuracy (LOO-CV 1000 Iterations)**} \\ \hline
**Hidden** & **DCT-5** & **DFT-5** & & \\
**nodes** & **mean** & **std** & **max** & **min** \\ \hline
20 & 80.70 & 0.024 & 87.96 & 73.15 \\
21 & 81.50 & 0.023 & 87.96 & 75.00 \\
24 & 82.08 & 0.022 & 88.89 & 75.00 \\
26 & 82.48 & 0.022 & 88.89 & 75.93 \\
28 & 82.89 & 0.022 & 89.82 & 75.00 \\
30 & 83.15 & 0.022 & 90.74 & 75.93 \\
32 & 83.41 & 0.023 & 91.67 & 75.00 \\
34 & **83.53** & **0.022** & **89.82** & **76.85** \\
36 & 83.40 & 0.023 & 89.82 & 75.00 \\
38 & 83.34 & 0.023 & 89.82 & 75.00 \\
40 & 83.48 & 0.023 & 89.82 & 75.93 \\
42 & 83.20 & 0.022 & 89.82 & 76.85 \\
44 & 83.20 & 0.024 & 89.82 & 75.00 \\
46 & 82.82 & 0.023 & 89.82 & 74.07 \\
48 & 82.57 & 0.023 & 89.82 & 75.00 \\
50 & 82.11 & 0.024 & 88.89 & 75.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The multi-class classification problem. Summary of the network accuracy obtained by using 10 features (five coming from the DCT and five from the DFT) depending on the hidden nodes used. The best results, highlighted in bold, are obtained when the net has 34 hidden nodes. The LOO-CV was performed and the experiments were repeated 1000 times. Figure 11: **(a)** The confusion matrix of binary-classification realization with an accuracy value of 95.7%. The results were obtained by performing LOO-CV using five features based on the DFT with a SLFN of 10 hidden nodes. The mean accuracy value after performing 1000 LOO-CV iterations was 89.3%. **(b)** The confusion matrix of multi-class classification realization with 85.2% accuracy. The results were obtained by performing LOO-CV using five features based on the DCT plus five more based on the DFT, with an SLFN of 30 hidden nodes. The mean accuracy value after performing 1000 LOO-CV iterations maintaining the same SLFN size was 83.15%. The first step was to reduce the images to a size of 384 \(\times\) 512 pixels. For training, we used the 40 images without marks, and we indicated the region of interest found with the specialist's marks. This region of interest is a rectangle that is specified with the two extreme points on its main diagonal. We employed the Faster R-CNN object detector provided by MATLAB [49; 50]. A Faster R-CNN object detection network is constituted of a feature extraction network followed by two subnetworks.