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@article{186774,
        author = {Mr. Ahire Himanshu Sachin and Prof. Gulhane V. R. and Abhale B.A and Dr. Rokade P. P and Mr. Lokhande Ravindra Ramesh and Miss. Sonawane Shweta somnath and Miss. Shah Zaveriya Naaz liyakat Ali},
        title = {Deep Learning-Based Pediatric Malnutrition Detection: A Systematic Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2897-2903},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186774},
        abstract = {Malnutrition in children remains a major public health concern, particularly in low-resource regions where clinical evaluations and growth monitoring tools are limited. Conventional diagnosis relies on anthropometric measurements and medical assessment, which can be time-consuming, error-prone, and inaccessible in remote areas. This research introduces a deep learning-based visual diagnostic system designed to identify signs of malnutrition from pediatric facial and body images. The proposed approach employs convolutional neural networks (CNNs) to extract discriminative visual features such as facial asymmetry, muscle and fat depletion, bone prominence, and skin texture indicators associated with undernutrition. A curated dataset of pediatric images is pre-processed and augmented to enhance robustness, and model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The goal of this study is to provide an automated, accurate, and efficient screening tool that can support frontline healthcare workers and community health programs. The system demonstrates strong potential as a non-invasive, scalable solution to assist early detection of malnutrition, enabling timely intervention and reducing diagnostic dependency on specialized clinical infrastructure.},
        keywords = {},
        month = {November},
        }