## 4 Experimental Results  
### Experimental Results for Skin Lesion Detection Using Various CNNs  
To implement the CNN methodologies, we employed MATLAB. All CNN models were pre-trained on the ImageNet database, leading to results that rely on the principles of transfer learning. As previously indicated, each of the two databases (ISIC 2020 and DermIS) were treated separately. For training, we allocated 700 images, alongside 300 for validation and 200 for testing purposes. The training process encompassed several key aspects, including validation accuracy, iterations per epoch, maximum iterations, and the time taken for training, which was documented for all eight CNNs forming the comprehensive decision fusion system. The specifics of this training progress are summarized in Table 2. Our training configuration included 6 epochs, a learning rate of 0.0001, and a maximum of 276 iterations, with 46 iterations per epoch applied uniformly across all the CNNs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CNN** & **Database** & **Time Required** & **Validation Accuracy** \\ \hline \multirow{2}{*}{**DarkNet-53**} & DermIS & 10 min 29 s & 89.00\% \\  & ISIC & 38 min 35 s & 82.00\% \\ \hline \multirow{2}{*}{**Dense-Net-201**} & DermIS & 24 min 40 s & 81.00\% \\  & ISIC & 44 min 47 s & 77.00\% \\ \hline \multirow{2}{*}{**GoogLeNet**} & DermIS & 3 min 45 s & 89.00\% \\  & ISIC & 36 min 3 s & 77.00\% \\ \hline \multirow{2}{*}{**Inception-V3**} & DermIS & 9 min 30 s & 84.00\% \\  & ISIC & 39 min 30 s & 75.50\% \\ \hline \multirow{2}{*}{**Inception-ResNet-V2**} & DermIS & 25 min 21 s & 86.00\% \\  & ISIC & 49 min 0 s & 75.50\% \\ \hline \multirow{2}{*}{**ResNet-50**} & DermIS & 6 min 32 s & 85.00\% \\  & ISIC & 39 min 36 s & 80.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training and validation progress. Looking at Table 2, the results are quite revealing. For the ISIC 2020 database, DarkNet-53 achieved the highest accuracy, while for the DermIS database, GoogLeNet came out on top. In terms of training duration, Inception-ResNet-V2 on the ISIC 2020 database required the longest training time (49 min and 0 s), whereas GoogLeNet on the DermIS database boasted the shortest training time (3 min and 45 s). Notably, many of the CNNs exhibited improved training times when applied to the DermIS database compared to their performance on the ISIC 2020 database. This observation might relate to the database's quality and characteristics. Numerous factors can impact the duration of the training, but our initial focus when addressing this aspect is the determination of an appropriate learning rate for each CNN. It is established that reducing the learning rate prolongs the CNN's learning process while excessively elevating it can cause weight divergence, ultimately diminishing training quality. As a prospective endeavor, we intend to devise an efficient method for identifying suitable learning rates for individual CNNs. This is important because assessing various constant learning rates for each CNN is both time-intensive and prone to errors. To compute the F1 score, we employed MATLAB to generate a confusion matrix for each CNN. The F1 score was then calculated using the formula provided in Table 1.  
Based on Figure 18, we can observe that for the DermIS database, DarkNet-53 achieved the highest F1 score of 0.8899, closely followed by GoogLeNet with a score of 0.8898 and Inception-ResNet-V2 with a score of 0.859. On the other hand, for the ISIC 2020 database, DarkNet-53 once again stood out with the highest F1 score of 0.818. Following DarkNet-53, ResNet-50 and DenseNet-201 attained F1 scores of 0.804 and 0.77, respectively. Once more, we can note that a majority of CNNs yielded superior outcomes when employed on the DermIS database compared to the ISIC database. This discrepancy further underscores the distinction in image quality between these two databases. Enhanced image clarity, characterized by minimal noise, corresponds to heightened performance levels.