Enhanced Nail Image Analysis for Early Disease Detetion

Abstract

Nail health serves as an important diagnostic indicator of an individual’s overall well- being, often revealing early signs of systemic conditions such as diabetes, cardiovascular disorders, vitamin deficiencies, and fungal infections. Motivated by the diagnostic value embedded in nail appearance, the proposed system, Nail Insight, introduces an intelligent and non- invasive approach for early disease detection through advanced nail image analysis. The system leverages the combined strengths of image processing and deep learning to achieve accurate and accessible assessments. High- resolution nail images are first captured using standard digital cameras or smartphone devices, ensuring user convenience and broad applicability. These images undergo a robust preprocessing pipeline involving noise reduction, contrast enhancement, and edge refinement to ensure clarity and improved feature visibility. This preprocessing step is crucial for optimizing the quality of input data and enhancing the reliability of subsequent computational operations. Following this, the system applies sophisticated segmentation algorithms to precisely isolate the nail region from surrounding skin and background artifacts. By focusing solely on the relevant region of interest, the model can effectivelyextractmeaningfulfeaturesfor classification. Ultimately, Nail Insight aims to deliver a reliable, user-friendly diagnostic tool capable of identifyingnail abnormalities early, thereby supporting timely medical intervention and improved health outcomes.