Exploratory Analysis of Skin Cancer Dermatoscopic Image Datasets and Classification Methods

Abstract

Skin cancer is a critical global health issue, where early detection significantly improves treatment outcomes. In this review paper, titled Exploratory Analysis of Skin Cancer Dermatoscopic Image Datasets and Classification Methods, we systematically explore the intersection of artificial intelligence (AI) and skin cancer diagnosis. Our approach began with a detailed literature survey of 40 research studies, providing insights into advancements and challenges in AI-based classification methods. This survey emphasizes the role of machine learning algorithms, particularly convolutional neural networks (CNNs), and datasets such as HAM10000, ISIC 2019, and ISIC 2024 in improving diagnostic performance. Next, we conducted exploratory data analysis (EDA) on the HAM10000, ISIC 2019, and ISIC 2024 datasets, uncovering critical patterns related to lesion distribution, anatomical sites, and demographic factors. These analyses highlight biases and imbalances in the datasets, which are crucial to address for robust model training. Finally, we discuss the creation and evaluation of a machine learning model trained on a separate dataset. Initial experiments revealed challenges such as overfitting and class imbalance. Through advanced data augmentation techniques and the integration of an Augmentor pipeline, we mitigated these issues, achieving improved accuracy and generalizability. This paper provides a comprehensive framework for integrating literature insights, dataset analysis, and iterative model improvement to develop effective AI-based solutions for skin cancer detection. It underscores the importance of addressing dataset biases, adopting diverse datasets, and refining methodologies to advance AI applications in dermatology.