Detection of Malnutrition in Children Through Visual Diagnosis of Pediatric Images Using Deep Learning

Detection of Malnutrition in Children Through Visual Diagnosis of Pediatric Images Using Deep Learning

IEEE BASE PAPER TITLE:

Detection of Malnutrition in Children Under 5 Years of Old Using Deep Learning

IEEE BASE PAPER ABSTRACT:

This study evaluates the performance of Convolutional Neural Networks (CNNs) for the early detection of childhood malnutrition. The architectures analyzed include ResNet-50, EfficientNet-B4, VGG16, and AlexNet, selected for their demonstrated effectiveness in image classification tasks.

Images were categorized into three classes—normal, at risk of malnutrition, and severely malnourished—and underwent preprocessing steps such as resizing, flipping, zooming, and face detection using MTCNN.

The evaluation employed metrics including accuracy, precision, recall, and F1-score, with ResNet-50 emerging as the most effective model, achieving an accuracy of 92%. Based on these findings, this study explores the potential application of ResNet-50 in mobile solutions to provide accessible and practical tools for malnutrition detection in resource-limited settings.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

ResNet152V2, MobileNet.

OUR PROPOSED PROJECT ABSTRACT:

Malnutrition remains a critical public health concern, particularly among children, necessitating early and accurate diagnostic methods to support timely intervention. This project presents a deep learning-based system for the automatic detection of malnutrition in children using full-body pediatric images. The system classifies images into two categories: Malnutrition and Nutrition, leveraging Convolutional Neural Network architectures. The application is developed using Python for backend processing and Flask as the web framework, with a responsive interface built using HTML, CSS, and JavaScript.

Two state-of-the-art deep learning models, ResNet152V2 and MobileNet, were trained and evaluated independently on a balanced dataset comprising 2,010 images (1,005 for each class).

The image preprocessing pipeline includes rescaling, resizing, data augmentation, directory-based loading, batching, shuffling, and training-validation splitting, ensuring robust training and model generalization.

The ResNet152V2 model achieved a training accuracy of 99% and test accuracy of 96%, while the MobileNet model recorded a training accuracy of 99% and test accuracy of 95%.

For performance evaluation, standard classification metrics such as Precision, Recall, F1-score, and Confusion Matrix were utilized. Additionally, model accuracy and loss graphs were plotted to visualize training performance across epochs for both models.

The results demonstrate that deep learning can effectively distinguish between malnourished and nourished children using visual cues alone, offering a scalable and non-invasive approach to nutritional screening in clinical and rural settings. This system has the potential to support healthcare professionals and aid in the early detection of malnutrition, thereby improving pediatric health outcomes.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 20 GB.
• Monitor : 15’’ LED.
• Input Devices : Keyboard, Mouse.
• Ram : 8 GB.

SOFTWARE REQUIREMENTS: 

• Operating System : Windows 10 / 11.
• Coding Language : Python 3.12.0.
• Web Framework : Flask
• Frontend : HTML, CSS, JavaScript.

REFERENCE:

Cliver Aguilar; Joel Tucta; José Santisteban, “Detection of Malnutrition in Children Under 5 Years of Old Using Deep Learning”, 2025 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), IEEE Conference, 2025.

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Frequently Asked Questions (FAQ’s) & Answers:

1. What is the objective of this project?

The objective of this project is to develop an AI-based system that automatically detects whether a child is malnourished or healthy by analyzing full-body images using deep learning techniques.

2. What type of images are used for training the model?

The system uses full-body images of children rather than facial images. These images are categorized into two classes: Malnutrition and Nutrition.

3. Which deep learning models are used in this project?

Two convolutional neural network (CNN) architectures are used: • ResNet152V2 • MobileNet Each model is trained separately and evaluated independently.

4. What accuracy do the models achieve?

• ResNet152V2: 99% training accuracy, 96% testing accuracy • MobileNet: 99% training accuracy, 95% testing accuracy

5. How is the dataset structured?

The dataset contains a total of 2,010 images: • 1,005 images labeled as Malnutrition • 1,005 images labeled as Nutrition

6. What preprocessing steps are applied to the images?

The following preprocessing steps are applied: • Image Rescaling • Image Resizing • Data Augmentation • Train/Validation Split • Batching and Shuffling

7. Which programming languages and technologies are used to build the system?

• Backend: Python • Frontend: HTML, CSS, JavaScript • Web Framework: Flask • Libraries: TensorFlow

8. How is the performance of the models evaluated?

The performance is evaluated using: • Precision • Recall • F1-Score • Confusion Matrix Additionally, model accuracy and loss graphs are used to visualize training behavior.

9. Is this system available as a web application?

Yes, the project includes a Flask-based web application that allows users to upload a child’s image and receive real-time classification results.

10. Can this system be used in real-world scenarios like hospitals or rural health centers?

Yes, the system is designed to be user-friendly and can be deployed in healthcare settings. However, it should be used as a supportive diagnostic tool, not as a replacement for professional medical evaluation.