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

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.

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

Local binary pattern (LBP) , Block Chain Technology (BCT) , Machine Learning (ML)