## 2 Related Work
Lastly, in [33], two decision fusion methodologies based on CNN models were introduced, involving the extraction of Regions of Interest (ROI) using the Geometric Active Contour (GAC) algorithm. Extracted features were then employed for classification through Random Forest (RF) and NN. The second method included the fusion of features from various CNN models, yielding an accuracy of 96.1%, an ACC of 94.41%, a precision of 88.69%, a sensitivity of 88.9%, and a specificity of 99.44% [33]. In [34], a model is presented that utilizes level-set segmentation for effectively segmenting images with non-uniform properties. This model, featured in the current research, boasts exceptional attributes, including high accuracy, speed, rapid convergence, resilience to varying starting contours, and resistance to noise interference. The proposed approach delivers results characterized by a true detection rate of 94%, a false negative rate of 3.62%, and an error rate of 3.3%. The testing outcomes substantiate the assertion that the recommended method for lesion segmentation is robust and remains unaffected by factors such as hair, blood vessels, changes in brightness, and variations in contrast. In [35], the authors explore the application of various machine-learning techniques to construct a high-performance ensemble classifier for six distinct skin lesions. Specifically, the researchers utilize Adaboost, voted ensemble, random forest, boosted Gaussian Mixture Model (GMM), voted Convolutional Neural Network (CNN), and boosted Support VectorMachine (SVM). The authors recognize an issue of imbalanced classification across all classes, prompting them to adopt ensemble transfer learning and transfer learning techniques. This strategic choice effectively mitigates the challenge posed by class imbalance. The study encompasses a total of 19 classifiers, which include 5 ensemble transfer learning models, 6 ensemble models, 4 Machine Learning (ML) and Deep Learning (DL) models, and four transfer learning models. Performance metrics are thoroughly assessed to gauge the effectiveness of each classifier. In the paper's conclusions, the authors note that the Ensemble voted, GMM, and random forest classifiers exhibit comparatively lower performance than the Adaboost and boosted SVM classifiers. Notably, the ensemble CNN achieves an impressive accuracy rate of 98.67% [35]. In conclusion, the field of melanoma detection through CNNs and DL techniques has seen significant exploration over the years, with various methodologies offering promising results. However, the challenge remains open due to the need for systems trained on sufficient images that ensure high accuracy and minimal chance of misclassification. These studies explore various methodologies, architectures, and databases for the purpose of identifying skin lesions, particularly melanoma. Each study tackles the challenge of accurate detection from different angles, often achieving promising results. However, despite the multitude of efforts, the researchers point out that achieving high accuracy and minimizing false detections remains an ongoing challenge due to factors such as dataset size and complexity. In contrast, our paper takes a distinct approach by focusing on enhancing the performance of melanoma detection through a multi-network system utilizing decision fusion. We consider several high-performance CNN architectures, including DarkNet-53, DenseNet-201, GoogLeNet, Inception-V3, InceptionResNet-V2, ResNet-50, ResNet-101, and Xception. The selection of these networks is guided by specific criteria outlined in the methodology section, indicating a methodical approach to network selection. The key innovation lies in the establishment of decision fusion weights based on F1-scores from each CNN combined with the utilization of specific criteria to select the CNN demonstrating the highest performance based on state-of-the-art analysis. By leveraging these scores, we optimize the performance of our proposed system.