## 5 Experimental Results
### Comparison with Studies in the Literature
Furthermore, for a categorization task, they combined the FCRN with several deep residual networks. [PERSON] et al. [32] discovered a technique for combining two types of features of a deep CNN, namely, global and local features. To retrieve these features, they used a deep residual network and a bilinear pooling approach. [PERSON] et al. [39] introduced a technique based on the ResNet model and a local descriptor encoding mechanism. To train the pre-trained ResNet, they employed a huge natural ImageNet dataset. Next, based on Fisher vector (FV) encoding, these local deep descriptors were combined to create a global image representation. Lastly, using an SVM, the FV representations were employed to diagnose melanoma. [PERSON] et al. [40] discovered a technique for jointly running deep CNNs and allowing them to train on one another. Each pair of neural networks' learnt image representations was combined as the entry of a network that had a fully connected design that forecasted if the pair of image data belonged to the same category. Therefore, if one of the neural networks correctly classified, a fault made by the other caused a synergic mistake, which required an additional effort to modify the network. This network can be constructed from start to finish using classification faults from each network. [PERSON] et al. [41] suggested a cross-net-based combination of several fully convolutional networks to detect skin lesions on its own. They used multiple convolutional networks to select semantic regions and local colors and patterns in skin images. In addition, they used FV to encode the selected features. Figure 8: Comparison of the time taken by different optimizers on the four datasets. For the PH2 dataset, the following technologies for melanoma diagnosis were evaluated together. One involved an artificial neural network, as introduced in [60]; in addition, they developed a decision-support system. Sparse kernel models for representing feature data in a high-dimensional feature vector were proposed by [61]. They developed a method for segmenting and classifying lesion images based on sparse representations. They also employed discriminative sparse kernel coding to simultaneously learn a kernel-based dictionary and a linear classifier. According to [62], U-Net can be used to automatically detect malignant tumors. To tackle the limitation of overfitting, U-Net was employed with a spatial dropout, and several augmentation techniques were added to the training examples to ensure more samples. As part of their IoT framework, the authors of [63] employed transfer learning and CNNs. They used convolutional neural networks (CNNs) as resource extractors. They tested the effects of combining twelve CNN architectures with seven distinct classification models. A hierarchical architecture founded on two-dimensional pixels in the images and ResNet was introduced in [64] for advanced deep learning. They improved the quality of dermoscopy images by combining locally and globally enhanced images. Later, using TL, a ResNet approach was developed for these mapped images and learned features. The retrieved features were modified using the grasshopper optimization approach, and the Naive Bayes classification algorithm was used. The Chest-XRay dataset was used to compare various advanced methods for the detection of pneumonia. In [65], the authors examined the use of generative adversarial networks (GANs) to enrich a dataset by producing chest X-ray data samples. GANs offer a method for learning about the underlying architectures of medical images, which can subsequently be used to make high-quality and realistic samples. In [66], the authors