Detection of Malnutrition

Malnutrition is a critical global health issue, especially in developing countries, where early detection is essential to prevent severe health consequences. This project proposes an innovative system for detecting malnutrition in children using image analysis powered by machine learning. The system allows users, such as parents and healthcare workers, to upload images of children, which are then processed by a neural network to assess the likelihood of malnutrition. The system provides a confidence score and personalized health recommendations based on the analysis. The proposed solution integrates both hardware and software components, including a database for storing user information, images, and analysis results, along with a machine learning model trained to identify malnutrition signs. The system also provides aggregated regional statistics to help healthcare workers monitor malnutrition rates and address hotspots effectively. The use of structured data (SQL) for analysis results and unstructured data (NoSQL) for images ensures scalability and flexibility in data handling. By automating the detection process, this project aims to facilitate early intervention, improve health outcomes, and assist healthcare professionals in focusing their efforts where they are most needed. This approach can lead to significant improvements in managing and reducing malnutrition rates globally