## 3 Materials and Methods
### Methodology for Skin Lesion Detection Using the Decision Fusion Classifier System  
In our previous work [6], we introduced a decision fusion framework employing diverse classifiers encompassing Neural Networks (NN), feature-based techniques, and CNN. However, the selection process for these classifiers was not addressed. The fusion weights for this system were established solely on the basis of accuracy [6]. Building upon the insights gleaned from the literature review in Chapter Two, wherein numerous authors demonstrated remarkable outcomes using CNNs, our present endeavor introduces a novel decision fusion classifier. This classifier is designed to amalgamate multiple CNN architectures, thereby  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Indicators** & **Formula** \\ \hline
**Precision** & \(\frac{TP}{TP+TP}\) \\
**Recall** & \(\frac{TP}{TP+TN}\) \\
**Accuracy** & \(\frac{TP+TN+TN}{TP+FP+FN+TN}\) \\
**F1 score** & \(2\cdot\frac{Precision\cdot Recall}{Precision+Recall}+\frac{TP}{TP+\frac{1}{TP+ \frac{1}{TP+\frac{1}{TP+\frac{1}{TP+\frac{1}{TP+\frac{1}{TP+\frac{1}{TP+ \frac{1}{TP+\frac{1}{TP+\frac{1}{TP+\frac{1}{TP+\frac{1}{TP+\frac{1}{TP +\frac{1}{TP+\frac{1}{TP+\frac{1}{TP+\frac{1}{TP+\frac{1}{TP+\frac{1}{TP +\frac{1}{TP+\frac{1}{TP+{TP+\frac{1}{TP+{TP+{TP+{TP+{TP+{TP+{TP}}}}}}}}}}}}}}}}}}}\) \\ \hline \hline \end{tabstriving to attain the utmost performance levels for melanoma detection while also ensuring adaptability to varying databases. The selection of the eight CNN architectures was guided by a comprehensive set of criteria grounded in various considerations:  
* **Performance--the neural network has a proven track record of achieving high accuracy and performance on melanoma detection tasks and on the specific databases we used;**
* **Availability of pre-trained models on large-scale databases, such as ImageNet. Pre-trained models can significantly speed up the training process and provide a good starting point for transfer learning;**
* **Complexity--the neural network is not out of reach complex. The complexity of the CNN architectures should be taken into consideration. Complex models may require more computational resources and longer training times, which can be challenging in practical applications. Hence, it is important to strike a balance between complexity and performance;**
* **Generalization--the network should have a strong ability to generalize to unseen data and can be applied to a variety of different images. Robustness and generalization are crucial for real-world deployment;**
* **Diversity of architectural designs--in the selection of the neural networks, we took into consideration diverse design principles. Each architecture has different features and characteristics, such as the use of residual connections, inception modules, or dense connections. Including diverse architectures can help capture a wider range of melanoma characteristics and improve the chances of detecting different types of melanomas. It is beneficial to choose CNNs that exhibit diverse architectural designs and utilize different techniques. This increases the chances of capturing complementary information and avoiding bias or limitations that might be present in a single architecture;**
* **Previous success--the neural network has demonstrated exceptional performance in melanoma detection or similar medical imaging tasks [76; 77; 78; 79]. **  
**Certainly, these factors are profoundly influenced by the databases utilized for CNN training and the pre-processing methodologies implemented. To ascertain the optimal combination of CNN architectures for our specific application, an iterative approach involving experimentation and refinement is indispensable.