AI-Driven Early Skin Cancer Detection: A Game-Changer in Dermatology

Abstract

Early detection and treatment of skin cancer have an impact on the patient’s outcome, and therefore detecting skin cancer is very important. Artificial intelligence (AI) has started becoming a promising avenue for leveraging in this domain, revolutionizing the way it does traditional dermatology. In this paper, we investigate AI powered models such as VGG19, Xception, and InceptionV3 for skin cancer detection with a goal to evaluate how accurately these models can determine skin cancer and what implications AI can have in dermatology. For this paper, the AI models under study are VGG19, Xception, and Inception V3 to identify their effectiveness in distinguishing skin cancer from noncancerous conditions. We trained and tested these models on a diverse set of skin lesion images and report their performance: VGG19’s accuracy was 92.73% whereas Xception had 88.32% and Inception V3 reached 69.70%. Deep learning architectures prove high capacity to distinguish features of malignant and benign lesions, yet the paper considers possibilities of model bias and a dataset that can influence generalization. The findings justify the use of AI in improving dermatologists’ diagnostic strength and increasing the chances of early detection and management of these abnormalities. As a result, AI skin cancer detection also provides the opportunity to further increase access to better standard of care, including for geographic regions of deficit. Future study should aim to refine existing models and utilize ensemble methods and extend the pool of data for better generalization and apply them in the related ethical and regulatory concerns in clinical settings. Artificial intelligence assisted skin cancer detection also holds considerable promise for the practice of dermatology and the management of that common cancer! Ultimately AI powered skin cancer detection is a transformative force in Dermatology, which can and will redefine the standards of care and burden of this prevalent malignancy.