An Innovative Approach for Building Precise Convolutional Neural Network Models for Melanoma Detection

Abstract

Melanoma is one of the deadliest forms of skin cancer, and early detection is critical for improving patient outcomes. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have shown promise in automating melanoma diagnosis with high accuracy. This paper presents an innovative framework for building precise CNN models for melanoma detection. The framework incorporates advanced image preprocessing techniques, optimized CNN architectures, and thorough evaluation metrics to ensure robust classification performance. The proposed framework demonstrates improved diagnostic accuracy over traditional methods by utilizing extensive, well-curated datasets and implementing fine-tuning strategies for CNN parameters. Key factors such as image resolution, feature extraction, and model training are explored to highlight their impact on CNN performance. The results indicate that this approach offers significant potential for clinical applications, providing dermatologists with an automated, reliable tool for early melanoma detection. The framework contributes to ongoing efforts in medical imaging by addressing both computational efficiency and accuracy, thereby supporting real-time diagnostic processes in healthcare. A convolutional neural network classifier is a deep learning-based system that stratifies the extracted information.