Scanova: AI-Powered Skin Cancer and Dermatological Disease Detection System

This study shows that Artificial Intelligence, especially       deep learning models like CNN, is highly effective for skin disease and skin cancer detection. Compared to traditional methods, AI-based systems provide faster, non-invasive, and cost-efficient diagnosis. Research results indicate that CNN and transfer learning models can achieve high accuracy (around 80%–98%) depending on dataset quality and model design . However, several limitations still exist. Many models suffer from imbalanced datasets, lack of diversity in skin tones, and difficulty in distinguishing visually similar diseases. In addition, most systems are tested in controlled environments and lack proper clinical validation, making them less reliable for real-world use . Therefore, AI should be used as a support tool for dermatologists rather than a complete replacement. In future, the performance of these systems can be improved by using larger and more diverse datasets, applying better preprocessing and data balancing techniques, and integrating transfer learning and hybrid models. Development of mobile-based applications, explainable AI systems, and real-world clinical validation will further enhance usability and trust. Combining AI with medical expertise (Augmented Intelligence) will be the most effective approach for accurate and reliable skin disease detection.  

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