Non-invasive Point-of-care System for Disease Detection through Fingernail

There is an increased need for Point-Of-Care (POC) devices since it is challenging to diagnose some diseases in resource-constrained areas. In medical science, doctors carefully examine the nails to learn vital information about the patient's health. Since the human eye is limited in its ability to distinguish between colors and textures, digital image processing techniques are used to evaluate nail conditions. In this study, the health problems of a patient are identified using their fingernail. This study's main objective is to identify illness from a patient's fingernail using a non-invasive computational platform. An image-capturing unit has been developed to collect a dataset of nails. Three separate camera lenses were used to compare the images. The images were taken with a smartphone camera, CPL mobile phone camera lens and microscope smartphone app. The intensity profile of each image was plotted. It is determined that the intensity plots for all three images are comparable. The CPL camera lens is not accessible to everyone. Additionally, not every person's phone has a microscope mobile application. So, the analysis of identifying the disease from fingernails can be done on the images taken by any smartphone camera as it is easily available to everyone even in rural areas. Also, smartphone-based imaging systems may offer an affordable, and user-friendly alternative for disease detection. Then the fractal dimension was applied to healthy nails and liver-diseased nails captured by a smartphone camera. As fractal theory is based on calculating the roughness of the surface, it can be applied to nails for texture analysis. The texture of healthy nails is observed to be smooth and unhealthy nails have a rough texture. It is observed from our analysis that healthy nails have a fractal value between 1.3-1.5, while nails with liver disease have a fractal value between 1.81-1.84. This is completely a non-invasive technique and a cost-effective approach.