## 3 Materials and Methods
### Skin Lesion Detection Using Various CNNs  
We employed MATLAB R2023b to implement the various CNN methodologies described. The process of selecting software tools for a project entails a thorough assessment of several factors, which includes analyzing the particular task's requirements, the availability of relevant functions, and the ease of putting the solution into practice. In our situation, our choice to employ MATLAB and construct the decision fusion classifier within this environment stemmed from a blend of practicality, our existing expertise, and the distinctive features that MATLAB provides. Our team possesses a strong background in MATLAB, which has been developed over years of experience. This familiarity with MATLAB's programming language, built-in functions, and toolboxes allowed us to expedite the development process and focus on the core aspects of our research.  
MATLAB boasts an array of specialized toolboxes catered to diverse scientific and engineering domains. These toolboxes offer a comprehensive selection of functions and algorithms purpose-built for tasks such as data analysis, signal processing, and machine learning. Given the intricate decision fusion techniques involved in our project, the Signal Processing Toolbox and Statistics and Machine Learning Toolbox within MATLAB were invaluable in efficiently implementing these methods. The visual capabilities of MATLAB empower us to visualize and meticulously examine the outcomes of the decision fusion process. The capability to create insightful plots, graphs, and visual presentations enhances our comprehension of the classifier's performance and aids in conveying our findings to colleagues and stakeholders. MATLAB's programming environment affords a considerable level of flexibility when it comes to realizing intricate algorithms. This was especially advantageous for our decision fusion classifier, which necessitated complex data manipulation and fusion strategies. The comprehensive array of functions within MATLAB enabled us to fine-tune the implementation to our precise needs. MATLAB's interactive nature and immediate feedback loop expedite swift prototyping and experimentation. This proved pivotal during the iterative progression of our decision fusion classifier, allowing us to promptly evaluate various approaches, refine parameters, and evaluate performance. Furthermore, MATLAB's optimization capabilities, which encompass vectorized operations, streamlined the execution of our decision fusion classifier. This efficiency is of utmost significance, particularly when grappling with extensive datasets or algorithms demanding significant computational resources.  
The CNNs used were initially pre-trained on the ImageNet database. Therefore, the outcomes we achieved were based on the application of transfer learning techniques. These pre-trained CNNs already possess the ability to differentiate between various fundamental objects. To adapt these CNNs for skin lesion detection, we followed a sequence of steps. Initially, we fine-tuned the pre-trained CNNs to enable the classification of skin lesions into two categories: melanoma and non-melanoma. Subsequently, we initiated the data loading process, specifying the image locations. As elaborated in the preceding section, we partitioned the data into training and testing datasets, each accounting for approximately 70% and 20% of the data, respectively. Following this, we loaded the pre-trained networks. For network retraining, we substituted the final two layers with a new fully connected layer and a classification layer. To accelerate the learning process, we adjusted the weight learn rate factor by increasing its value. To facilitate the training procedure, image resizing was performed. All necessary training options were then configured. Validation imageswere subjected to classification, and we additionally generated a confusion matrix for each executed architecture [53; 54].