# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 2 Related Work  
Computational methodologies have been used for a long time to assist in the diagnosis of medical conditions. In the 1980s, computer-aided diagnostic (CAD) systems were developed to help dermatologists overcome challenges in diagnosing skin diseases, including skin cancer [8]. After 2000, machine learning-based approaches began to emerge. Techniques such as Support Vector Machines (SVM), which aim to find the optimal boundary between classes for data classification, k-nearest neighbor (k-NN), a non-parametric method that classifies data based on the closest neighbors in the feature space, and logistic regression, a statistical method used for binary classification by estimating the probability of a given input belonging to a particular class, were explored for diagnostic support. However, these methods did not achieve satisfactory detection performance due to high intra-class variation (where the lesions of the same classes can appear very different) and low inter-class variation (where the lesions of different classes can appear similar) [9; 10; 11]. Recently, various automation methods have been developed to classify skin cancer using skin images. Most of the previous studies have focused on developing various artificial intelligence algorithms to automate the diagnosis of different types of skin cancer, and studies on the detection, segmentation, and classification of skin lesions associated with skin cancer have been conducted using the CNN-based architecture. [PERSON] et al. [12] presented a ResNet model that solved the problem of losing gradients as the depth of the existing CNN model became deeper using a residual block. [PERSON] et al. [13] used CNN models and modified ResNet50, applying them to the HAM10000 dataset. They improved image quality using ESRGAN and addressed the class imbalance problem using data augmentation. They obtained results with 86% and 85.3% accuracy using CNN and ResNet50 models. [PERSON] et al. [14] presented DenseNet in 2017, a deep learning model in which each layer receives inputs from all previous layers to optimize information flow and gradient delivery, and prevents overfitting by reusing characteristics with high parameter efficiency. [PERSON] et al. [15] presented MobileNet in 2017, a lightweight network model for learning in resource-constrained situations. Using depthwise separable convolution operations instead of standard CNN convolutions helped to reduce the number of training parameters. In 2019, [PERSON] et al. [16] presented an EfficientNet that can achieve higher performance with fewer operations than other models using a complex scaling method that simultaneously scales the width, depth, and resolution of the model. In 2022, [PERSON] et al. [17] trained EfficientNet B0-B7 on the HAM10000 dataset by performing transfer learning on pre-trained ImageNet weights and fine-tuning the convolutional neural networks. The best model in this paper, EfficientNet B4, achieved a Top-1 accuracy of 87.91%. Also in 2022, [PERSON] et al. [18] added the MD-Net module to the DenseNet model for the ISIC 2019 dataset, achieving accuracies of 83.5%, 84.1%, and 83.8% for DenseNet121, DenseNet169, and DenseNet201. In 2023, [PERSON] et al. [19] performed a skin cancer classification task by extending the dataset and DMN using CycleGaN. Furthermore, to alleviate the gradient vanishing problem, he proposed a DMN to add connections and branches to the MobileNetV2 classification network, achieving an accuracy of 87.07%, which is higher than the 82.58% accuracy of the basic MobileNetV2 model. Deep learning algorithms that are not CNN-based are also used for image processing. In 2017, [PERSON] et al. [20] introduced the Transformer algorithm. This work revolutionized natural language processing and machine learning by introducing a novel architecture based entirely on self-attention mechanisms, eliminating the need for recurrent neural networks (RNNs). In 2020, [PERSON] et al. [21] extended this architecture to image processing with the development of Vision Transformer (ViT).