Publication

Abstract : Skin cancer is one of the most common types of cancer worldwide, and its early detection is crucial for effective treatment and improved survival rates. Traditional methods for diagnosing skin cancer, such as visual inspection by dermatologists, can be subjective and prone to errors. In recent years, artificial intelligence (AI) and machine learning (ML) techniques, especially deep learning, have shown great promise in automating the analysis of skin lesions for accurate diagnosis. This paper provides an overview of the current advancements in AI-driven skin cancer detection systems, focusing on deep convolutional neural networks (CNNs) and other machine learning models for classifying melanoma and non-melanoma skin lesions. When diagnosing diseases with the unassisted eye, dermatologists may make errors. Therefore, artificial intelligence-enabled image processing tools can aid dermatologists in making judgments and performing exams. Based on learned weights and results from the first tier, a perceptron-type classifier evaluates whether the lesion is melanoma at the second, objective tier. The second tier’s final decision relies on a backpropagation perceptron. This two-tiered approach enables transitions between different databases.