Malnutrition Detection using Convolutional Neural Network

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 syste...

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Bibliographic Details
Published in2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) pp. 1 - 5
Main Authors Lakshminarayanan, Arun Raj, B, Pavani, V, Rajeswari, Parthasarathy, Saravanan, Azeez Khan, A. Abdul, Javubar Sathick, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.03.2021
Subjects
Alexnet
Convolutional Neural Network
Deep learning
Instruments
Malnutrition
Manuals
Medical services
Pediatrics
Training
Transfer learning
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Summary: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.
DOI:10.1109/ICBSII51839.2021.9445188