International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Pneumoinsight: AI for Smart Lung Diagnosis

Author(s) Mr. P.K Sangameswar, Mr. Rohit Manoj, Mr. Sayanth V, Mr. Sibin Babu
Country India
Abstract —Pneumonia is a serious lung infection and one of the leading causes of illness and death globally, particularly affecting children, the elderly, and individuals with weakened immune systems. The diagnosis of pneumonia typically relies on chest X-rays that are interpreted by radiologists, a process that can be time-consuming and susceptible to errors. To address these challenges, this study introduces an AI-driven framework for pneumonia detection that utilizes a Region-based Convolutional Neural Network (RCNN) based on the VGG16 architecture. The model was trained on a dataset comprising 5,856 pediatric chest X-ray images, which included 4,273 cases of pneumonia and 1,583 normal cases. To enhance the model's performance and address class imbalance, preprocessing techniques such as pixel normalization and data augmentation (including horizontal flipping, zooming, and shearing) were employed. The model demonstrated high accuracy in classifying X-rays as either "normal" or "pneumonia," highlighting the potential of AI-assisted diagnostics to reduce delays and aid radiologists in their work. Future efforts will focus on exploring advanced architectures, such as Transformer-based models, and expanding the dataset to improve the model's generalizability.
Keywords Pneumonia Detection Convolutional Neural Networks (CNN) Region-based CNN (RCNN) VGG16 ,Data Augmentation ,Transfer Learning ,AI-assisted Diagnostics , Medical Image Analysis, Deep Learning in Healthcare , X-ray Classification
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-06
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.40224
Short DOI https://doi.org/g9dhvk
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