Skin AI - Virtual Dermatologist

Abstract

Skin cancer accounts for millions of diagnoses every year and is currently a major threat to health systems globally. Since it is a leading malignancy worldwide, a correct and early diagnosis is needed to improve chances of survival; more importantly, proper early-stage treatments would reduce the risk of severe morbidity as well as curtail healthcare spending. This paper introduces Skin AI, an Artificial Intelligence (AI)-based solution that uses Convolutional Neural Networks (CNNs) to address the challenges of skin cancer detection and classification. The system utilizes the MNIST dataset for skin cancer imagery, providing a reliable and efficient method for identifying various conditions, including melanoma, basal cell carcinoma, and benign lesions. The proposed framework integrates advanced data augmentation techniques to enhance model robustness, along with explainable AI features to enhance trust and transparency in clinical decision-making. Furthermore, it incorporates telemedicine integration, enabling remote diagnosis and improving access to dermatological care, particularly in underserved and rural areas. The solution is designed with a focus on precision, scalability, and usability, ensuring it meets the practical needs of both clinicians and patients. This study provides a comprehensive overview of the development process, including data preprocessing, model architecture, and optimization strategies, along with performance metrics such as accuracy, sensitivity, and specificity. Additionally, the potential impact of Skin AI as a transformative tool is discussed, emphasizing its role in democratizing dermatological care, reducing diagnostic disparities, and bridging critical gaps in global healthcare accessibility.

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