Document Type

Article

Publication Title

International Journal of Advances in Soft Computing and Its Applications

Abstract

Undernutrition occurs when there are deficiencies in essential vitamins, while overnutrition refers to consuming an excessive amount of nutrients, leading to issues such as obesity, diabetes, and related health problems. Malnutrition often stems from the economic and social status of parents. In children, malnutrition can significantly result in physical and mental growth issues. Therefore, there is a need for early prediction of malnutrition in children to mitigate its adverse effects. Children image comprises great amount of information which can be analyzed for distinguishing between a nourished or malnourished children. The success stories of convolutional neural networks in image classification are the motivation to develop a deep learning model for the classification. In this work a custom designed convolutional neural network model is proposed for classification of children into category nourished or malnourished. The dataset consists of 630 and 1530 nourished and malnutrition children’s images respectively. The model is trained on children’s images database that includes both web scraped images and synthetic images. The convolutional neural network model is optimized by selecting optimal activation function through experimentation. The model is trained for 100 epochs with Adam optimizer and various activation functions. CNN with Relu with weight decay activation function obtained considerably good results with accuracy of 93.44%, precision 0.89, 0.85 recall, 0.87 F1 score for nourished class; and precision 0.95, 0.96 recall, and 0.96 F1 score for malnourished category. The model obtained a good accuracy of 92.83% for Leaky ReLu with precision 0.8, 0.9 recall, 0.85 F1 score for nourished and precision 0.97, 0.94 recall and 0.95 F1 score malnourished. Further, to develop trust in the model visualization of activation maps, convolutional layers and filters are implemented.

First Page

85

Last Page

102

DOI

10.15849/IJASCA.240730.06

Publication Date

1-1-2024

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