# Skin Lesion Classification Using a Deep Ensemble Model
## 1 Introduction  
Skin cancer poses a significant threat, marked by abnormal and malignant proliferation of cells within the skin. The skin is composed of three primary layers: the epidermis, dermis, and hypodermis. The epidermis contains melanocytes, which produce melanin, a pigment that is particularly sensitive to ultraviolet radiation [1; 2]. Abnormal development of melanocytes can lead to melanoma, a highly malignant tumor. Dermoscopy is a valuable imaging technique that reduces skin reflection, allowing for detailed examination of structures within the dermis and epidermis. The heightened magnification and minimized reflection artifacts characteristic of this approach enhance the acquisition of detailed visual data from deeper layers of the skin, thereby enhancing systems used for computer-aided diagnosis. Skin abnormalities are typically classified into three main categories: basal cell carcinoma (BCC), melanoma, and squamous cell carcinoma (SCC). BCC originates from basal cells in the epidermis, while SCC arises from squamous cells in the upper epidermis. Melanoma is often regarded as the most hazardous form of skin cancer due to its propensity for rapid dissemination and its potential to pose life-threatening risks. However, early detection and treatment significantly increase the likelihood of successful outcomes. Early and accurate diagnosis is critical for effective treatment and improved patient survival rates. Traditional diagnostic methods rely heavily on dermatologists' expertise and are subject to variability in accuracy [3; 4]. Therefore, there is a pressing need for automated, reliable, and precise diagnostic tools to assist in skin lesion classification. Recent advancements in deep learning have shown great promise in the automated categorization of skin abnormalities [5; 6; 7]. Convolutional neural networks (CNNs), in particular, have achieved significant success in image recognition tasks. This study leverages the strengths of an ensemble of three state-of-the-art CNN architectures--VGG16, Inception-V3, and ResNet50--to classify skin lesions into BCC, SCC, and melanoma using the ISIC dataset [8], as shown in Figure 1. The ISIC dataset, a benchmark in dermatological research, presents a diverse collection of skin lesion images. However, the dataset is imbalanced, with certain lesion types underrepresented. To mitigate the issue of dataset imbalance, we utilize an oversampling method [9], which ensures that our deep learning models are trained with a balanced distribution of each lesion category. This approach aims to improve the models' performance and generalizability. In this paper, we detail the methodology for classifying skin lesions using the ensemble of VGG16, Inception-V3, and ResNet50 models. We describe the preprocessing steps, including the oversampling technique used to balance the ISIC dataset, and the training procedures for each model. We also evaluate the ensemble model's performance compared to the individual models and discuss the implications of our findings for clinical practice and future research. This work contributes to the expanding field of deep learning in medical diagnostics, highlighting the potential of ensemble methods to enhance classification accuracy and reliability in skin lesion analysis. By combining multiple CNN architectures and addressing dataset imbalance, this study aims to develop a robust solution for the automated classification of skin lesions. Our main contributions to this endeavor are as follows:  
1. An ensemble approach combining VGG16, Inception-V3, and ResNet50 is developed to classify skin lesions into basal cell carcinoma, squamous cell carcinoma, and melanoma. 2. This approach achieves superior classification performance compared to individual deep learning models, as demonstrated by comprehensive experiments using the ISIC dataset. 3. An effective oversampling technique is applied to address class imbalance in the ISIC dataset, enhancing model robustness and generalization. 4.