Abstract: Skin cancer is a prevalent and potentially life-threatening disease, making early detection crucial for effective treatment. In this study, we address the challenge of skin cancer detection using machine learning techniques. Leveraging a dataset of dermatoscopic images from the International Skin Imaging Collaboration (ISIC), we employ convolutional neural networks (CNNs) to classify images into malignant and benign lesions. Our approach involves preprocessing, model building, and evaluation to assess the model's performance in detecting skin cancer. We explore various architectures, including standard CNNs and augmented data models, to improve classification accuracy and mitigate the effects of class imbalance. Through experimentation and evaluation, we demonstrate the effectiveness of our methodology in achieving high accuracy in skin cancer detection. Furthermore, we analyze the model's performance, identify areas for improvement, and discuss the implications of our findings for future research and clinical applications in dermatology. Overall, our study contributes to the ongoing efforts in leveraging machine learning for enhancing skin cancer diagnosis and improving patient outcomes.

Keywords: skin cancer detection, machine learning, CNN, dermatoscopic images, ISIC dataset, classification, model evaluation, data augmentation, class imbalance, clinical applications.

| DOI: 10.17148/IARJSET.2024.11479