Fingernail analysis for early detection and diagnosis of diseases using machine learning techniques

Document Type : Research Paper


1 Department of CSE, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India

2 Department of Mathematics, Rathinam Technical Campus, Coimbatore, Tamil Nadu, India

3 Department of CSE, Bannari Amman Institute of Technology, Erode, Tamil Nadu, India

4 Department of Information Technology,Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India


Each and every human have unique fingernails. In the early days, the psychological conditions of the human body were reflected with the help of the growth situation of the surface of nails.  It is possible to diagnose human nails and predict the disease. Predicting the disease at the early stage helps in preventing the disease. In this proposed work, the image of the nail is taken from a microscopic image. The lunula and nail plate are segmented effectively using the image pre-processing techniques. Histograms of oriented gradients and local binary patterns are used to capture the characteristic value. Once after pre-processing various features of the nails are extracted using various machine learning algorithms such as Support Vector Machines, Multiclass Support Vector Machine, Convolution Neural Network along with an Optimization algorithm named Ant Colony Optimization to improve the efficiency of classification.


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Volume 13, Special Issue for selected papers of ICDACT-2021
The link to the conference website is
March 2022
Pages 61-69
  • Receive Date: 09 August 2021
  • Revise Date: 17 December 2021
  • Accept Date: 06 January 2022