# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 1 Introduction  
Cancer is one of the leading causes of disease-related deaths in humans. It is a type of disease characterized by the irregular cell growth cycle of a group of cells. Normal cells replicate themselves in a regular cycle, grow through the process of division, and regulate cell growth through cell death. Unlike normal cells, malignant tumors grow very fast and invade surrounding tissues, and are called cancer [1]. Cancer can occur in any part of the body and can spread to other areas. When it affects the outermost layer of the skin, it is referred to as skin cancer. Skin cancer is primarily caused by chronic UV damage to skin cells from exposure to UV rays [2]. It is one of the few cancers with a steadily increasing incidence rate. If detected early, skin cancer can be successfully treated in nearly 95% of cases [3]. However, treatment becomes more challenging in later stages. Early detection and regular self-examination are vital, yet distinguishing malignant tumors from benign ones is challenging, as they often look similar to the naked eye. Skin lesions are diagnosed by medical domain experts, such as dermatologists, but this relies on the individual's subjective expertise and judgment. It was found that the disagreement among individual scholars regarding the classification of skin cancer reached 25% [4]. To reduce subjectivity and provide more robust diagnoses, AI-based models, including machine learning and deep learning algorithms, are being utilized to support specialists in strengthening their decision-making. Most existing AI-based skin lesion diagnosis methods are based on spatial information. In particular, Convolution Neural Networks (CNNs)have achieved notable success in medical analysis. Various CNN-based models have been developed, and ensemble models that combine the strengths of different architectures have also been introduced. In 2023, an ensemble model combining DenseNet121 and MobileNet achieved promising results [5]. While several ensemble-based skin lesion classification models have been proposed, most models rely heavily on CNN architecture and their convolutional operations. Furthermore, ensemble research utilizing the Transformer algorithm, which has recently appeared and is achieving excellent performance in various fields, has been limited. Therefore, this study incorporates one of the Vision Transformer (ViT) series, the ViT-B/16 model, leveraging the attention mechanism of the Transformer in the ensemble model. However, since the ViT-B/16 model is known for its high performance on large datasets, it did not outperform CNN models on the HAM10000 dataset, which is relatively small, limiting its effectiveness in building an ensemble model [6]. To address this issue, we enhanced the performance by applying data augmentation, allowing ViT-B/16 to learn the patterns better. Next, we apply the ABCD rule, a clinical criterion for assessing skin cancer, to our model. The ABCD rule is a commonly used tool in dermatology to help identify suspicious moles or skin lesions that could indicate melanoma, a serious form of skin cancer. This method assesses the following features: Asymmetry (A) assesses whether the two halves of a lesion are symmetrical. Border (B) checks if the edges of the lesion are irregular or well-defined. Color (C) examines whether the lesion displays uneven color patterns or a mix of multiple shades. Diameter (D) helps to determine if the lesion is notably large, which may indicate malignancy. Previous studies often employed methodologies that excel in general image classification but did not account for the medical characteristics of skin lesions. In this research, we integrate various data preprocessing methods that align with the medical ABCD rule, ensuring that these important dermatological factors are reflected in the model. Note that, due to the nature of our data, the actual diameter of the lesion is unknown, so D (diameter) is not considered. In summary, we propose a novel ABC ensemble model for skin lesion classification.