Deep Learning Models for Micronutrient Deficiency Detection Using Fingernails

• Published in: International journal for research in applied science and engineering technology 2024-06, Vol.12 (4), p.1501-1508
• Main Author: Srinivas, Kalyanapu
• Format: Article
• Language: English
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2024.59602

Despite being common, micronutrient deficiencies are frequently missed because of the shortcomings of traditional diagnostic techniques. Conventional evaluations take a lot of time and may involve intrusive techniques. In order to overcome these obstacles, this project offers a novel approach that makes use of EfficientNetB1 and deep learning to reliably identify micronutrient deficiencies from fingernail images. This non-invasive, effective solution offers a substitute for widespread health monitoring.In order to identify tiny visual cues indicative of different micronutrient deficits, our suggested technique entails training an EfficientNetB1 model on a large dataset of fingernail photos. The model's remarkable 99% accuracy rate shows how well it can detect and categorize dietary deficits in a variety of nutrient categories. Through the utilization of EfficientNetB1, we are able to optimize the micronutrient deficiency detection process, providing a scalable and easily accessible approach with significant potential for population-wide health screening. In spite of resolving the widespread problem of underdiagnosed micronutrient deficiencies, this ground-breaking research transforms the diagnostic environment by bringing a streamlined and effective technique. We overcome the drawbacks of conventional diagnostic methods by improving the precision and speed of micronutrient insufficiency detection from fingernail photos by utilizing deep learning and the enhanced capabilities of EfficientNetB1. Our method is accessible due to its non-invasive nature, which makes it a good substitute for general health monitoring.