Nail Disease Detection System

Author(s)	Sagari J, Samrudhi Shetty, Shwetha K R, Thanvi H V, Mrs Amrutha Bhat
Country	India
Abstract	Nail diseases are becoming an increasing health concern and can sometimes indicate underlying medical conditions. This paper presents a deep learning–based system developed to detect and classify nail diseases using digital images. The proposed approach uses the ResNet-18 Convolutional Neural Network (CNN) architecture to analyze nail images and identify disease patterns based on visual characteristics such as color, texture, and shape abnormalities. The residual learning capability of ResNet- 18 helps the model extract important features efficiently while reducing training complexity, leading to improved accuracy and faster convergence. The system is implemented as a user-friendly web application using the Flask framework, where users can upload nail images and receive diagnostic results in real time. By providing an accessible and reliable tool, the system aims to assist both healthcare professionals and individuals in the early detection and monitoring of nail-related conditions, thereby supporting preventive healthcare and improving dermatological assessment.
Keywords	eep Learning, Convolutional Neural Network (CNN), ResNet-18, Nail Disease Detection, Nail Image Classification, Healthcare AI
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-24

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International Journal For Multidisciplinary Research