## 4 Results  
In this study, an assessment of state-of-the-art pre-trained models for the task of classification of tomato plant diseases using images was done. The objective of this research was to compare the CNN models evaluating the accuracy, precision, sensitivity, specificity, F-Score, and AUC by fine-tuning. The results are shown in Table 6.  
All of the models showed a similar and statistically significant performance. Starting with the AUC, Inception V3 and ResNet 18 had the lower results with 99.2%, followed closely by AlexNet with 99.28%, ResNet 50 with 99.55%, and the best AUC result with GoogleNet, which obtained 99.72%, representing an almost excellent classification. On the other hand, in the accuracy metric, Inception V3 obtained the lower result with 98.65%, followed by AlexNet, ResNet 18, and ResNet 50 with 98.93%, 99.06%, and 99.1 5%, respectively, and the highest result was obtained by GoogleNet with 99.39%. In the same way, the order of the precision results were equal to the previous measure, where the lower percentage was obtained by Inception V3 with 98.28% and the highest by GoogleNet with 99.26%. Finally, in the sensitivity, specificity, and F-score measurements, the new Inception V3 performance was poor with 97.84%, 99.84%, and 98.05%, correspondingly, in opposition to GoogleNet, which got the best percentage in the previous metrics with 99.12%, 99.93%, and 99.20%, respectively. Indeed, as is shown, all of them achieved statistically significant performance in each measure, but the GoogleNet implementation achieved the highest percentage. In addition, based on the processing time it took for each CNN to carry out the classification process, AlexNet showed the best performance by taking the shortest time, which implies a better efficiency compared to the other architectures, remarking that its disadvantage in statistical measures is not significant compared to the results of the other CNNs, so it could be considered to sacrifice a minimum decrease in accuracy in exchange for greater processing efficiency. Furthermore, Table 7 presents the confusion matrix of the model with the best outcome based on the performance measures, which is GoogleNet. Depending on the results, it is possible to visually evaluate the performance of the classifier and to determine which classes are highlighted by the neurons of the GoogleNet model. The rows are related with the output class, while the columns \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Performance Measures** & **AlexNet** & **GoogleNet** & **Inception V3** & **ResNet 18** & **ResNet 50** \\ \hline Accuracy & 98.93 & 99.39 & 98.65 & 99.06 & 99.15 \\ Precision & 98.74 & 99.29 & 98.29 & 98.76 & 98.94 \\ Sensitivity & 98.38 & 99.12 & 97.84217 & 98.67 & 98.72 \\ Specificity & 99.88 & 99.93 & 99.85 & 99.89 & 99.90 \\ F-Score & 98.54 & 99.20 & 98.05 & 98.71 & 98.82 \\ Area under the curve (AUC) & 99.28 & 99.72 & 99.20 & 99.20 & 99.55 \\ Time (min) & 94.17 & 133.15 & 649.85 & 147.73 & 476.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance measures (%) for every pre-trained model. are related to the true class. The diagonal cells are associated to the observations that are correctly classified, and the off-diagonal cells correspond to the incorrectly classified observations. Among the ten classes, two to ten produced 100% correct classification results since those diseases have distinctive appearance and features when compared to the other classes, which are mosaic virus and the healthy class. Accordingly, the performance measures were calculated for each class, and the obtained results using GoogleNet model are shown in Figure 4.  
Figure 5a presents the multiclass ROC curve obtained based on the performance of the GoogleNet model with an AUC value of 99.72%. On the other hand, Figure 5b shows the performance of each class; as shown in the Figure, each class presents a higher fitness.