Neural Network Based Skin Cancer Classification from Clinical Images: Accuracy and Robustness Analysis

Abstract

This study aims to investigate the use of neural networks in classifying skin cancer from clinical images, with a specific emphasis on evaluating accuracy and robustness. Skin cancer, melanoma included, poses a significant worldwide health challenge, and early detection is vital for enhancing patient prognoses. Conventional diagnostic approaches heavily depend on clinical expertise, which can be subjective and inconsistent. The progress in deep learning, especially convolutional neural networks (CNNs), presents a promising alternative by automating skin lesion classification with high accuracy. The research involves developing a neural network model using a varied set of clinical skin images, enabling it to distinguish between benign and malignant lesions. Multiple architectures are tested, and their effectiveness is assessed using standard metrics such as accuracy, precision, recall, and F1-score. Beyond measuring overall accuracy, the study emphasizes robustness by evaluating the model in challenging conditions, including variations in illumination, obstructions, and diverse skin tones. Results indicate that neural networks can achieve superior accuracy in skin cancer classification, often outperforming traditional diagnostic techniques. However, robustness remains a crucial area for enhancement, particularly in real-world applications where image quality and patient diversity can fluctuate significantly. By examining the strengths and weaknesses of neural network-based models, this research underscores the potential of AI in clinical diagnostics while highlighting the necessity for further improvements in model generalization to ensure reliable implementation in healthcare settings.