Malnutrition Detection using Convolutional Neural Network
L. Arun Raj, B. Pavani, V. Rajeswari, Saravanan Parthasarathy, Asif Khan, K. Javubar Sathick
Abstract
Malnutrition is directly or indirectly responsible for the deaths of children younger than 5 years in many countries. Identification of malnourished children will help to prevent the risk of death and can reduce physical and health issues by taking necessary measures or treatment. The proposed system uses a Convolutional Neural Network (CNN), a Deep Learning algorithm that takes input, analyzes the images, and differentiates one from the other. The architecture we used here is AlexNet for the training process and Transfer Learning. The system takes the image of a child as the input and classifies the image into a malnourished or normal child by comparing the image with the trained model. The objective of the system is to detect malnutrition in children that can help people and healthcare providers to reduce the effects caused by malnutrition by automation implementation instead of a manual process.