# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 2 Related Work
By applying the Transformer, originally designed for natural language tasks, to image classification, ViT achieved competitive performance with convolutional neural networks (CNNs), especially when trained on large datasets. In 2024, [PERSON] et al. [22] evaluated the performance of the Vision Transformer (ViT) and Swin Transformer in classifying mobile-based oral cancer images. The results indicated that the Swin Transformer achieved higher accuracy compared to the ViT model, and both Transformer models outperformed the conventional convolutional model, VGG19. In addition, studies using existing CNN models [23; 24; 25] have continued for the skin lesion classification task, and the ensemble method has been used as one of the approaches. The ensemble method gained higher accuracy by utilizing the diversity of individual models. Ensemble models initially gained prominence in other application scenarios before being applied to medical imaging tasks. [PERSON] et al. [26] proposed a supervised learning framework to assess the data quality in marine sensor networks, addressing the class imbalance issue using an ensemble classifier system. Experimental results showed that the ensemble classifier balanced the classification accuracy between majority and minority classes, achieving 16.24% and 15.29% higher accuracy compared to individual classifiers. [PERSON] et al. [27] explored using ensemble learning to improve multilabel binary classification in sentiment analysis. The study compared a baseline stacked ensemble with a proposed weighted ensemble and tested them on two datasets (Semeval2018-Task 1, Toxic Comment). The weighted ensemble outperformed the stacked ensemble in 75% of cases, with performance improvements ranging from 1.5% to 5.4%. [PERSON] et al. [28] proposed a method to improve textile fabric defect classification accuracy using artificial intelligence. They applied an ensemble learning-based CNN model (ECTFDC) on an enhanced TILDA database to classify fabric defects. Experimental results showed that ECTFDC achieved a precision of 97.8% and a recall of 97.68%, outperforming other models. [PERSON] et al. [29] presented a method for Traffic Sign Detection and Recognition (TSDR), emphasizing the use of an ensemble of Convolutional Neural Networks (CNN) for sign recognition. The ensemble approach, implemented using TensorFlow, achieved over 99% recognition accuracy on circular signs from the Belgium and German datasets. This use of CNN ensembles significantly enhanced Advanced Driver Assistance Systems (ADAS) for safer driving environments. In particular, the ensemble method of the CNN model was also used in the medical classification task because it improved overall performance while reducing overfitting and increasing the robustness of predictions [30]. [PERSON] et al. [31] proposed an ensemble CNN model for the early diagnosis of COVID-19, incorporating DenseNet169, VGG16, and Xception. The ensemble model demonstrated the best performance, achieving an accuracy of 97.7%, a precision of 97.7%, a recall of 97.8%, and an F1-score of 97.7%. The ensemble model outperformed individual CNN models. [PERSON] et al. [5] normalized the skin lesion images to select lightweight CNN models and two pre-trained CNN models such as DenseNet121 and MobileNet, resulting in 91.12% accuracy. [PERSON] et al. [32] designed a deep ensemble learning model for skin cancer diagnosis. They utilized the EfficientNet, SENet154, and ResNet models, preprocessing each with a grayscale, segmented mask, and a multiplication image of the original image plus the segmented image, respectively, and then ensembled them to achieve 92.6% accuracy. [PERSON] et al. [33] proposed a multi-scale, multi-CNN (MSM-CNN) ensemble approach for skin lesion classification. By combining three CNN architectures (EfficientNet-B0, EfficientNet-B1, and FeSeNet-50) trained on dermoscopic images at various scales, the ensemble achieved a balanced multi-class accuracy of 86.2% on the ISIC 2018 test set. [PERSON] et al.