Effect of Quantum Machine Learning Algorithm in Preliminary Detection of Nail Diseases of Student

Abstract

In various countries facing many disorders are identified in the initial stages of diagnosis by analyzing the human hand’s nails. The different colour of the human nail’s can aid in diagnosing certain medical conditions. Nail diseases, including fungal infections, psoriasis, and melanonychia, are among the earliest indicators of systemic disorders. Accurate early diagnosis is crucial to prevent complications. Traditional machine learning (ML) techniques have made considerable progress in image-based diagnosis; however, their computational limitations restrict real-time, large-scale deployment. This paper explores the application of Quantum Machine Learning (QML) algorithms for the preliminary detection of nail diseases using image datasets. A hybrid quantum-classical approach was developed using Variational Quantum Circuits (VQC) integrated with classical convolution layers. Performance metrics demonstrate improved accuracy and computational efficiency over classical counterparts, indicating QML’s transformative potential in dermatological diagnostics.

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