Authors : Deepa B.; Rithvik M.; Ruben Raj L.

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/43p5zupx

Scribd : https://tinyurl.com/muadhspk

DOI : https://doi.org/10.38124/ijisrt/26jan1599

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Deep Lung delivers a next-generation approach for diagnosing pneumonia through CNNs developed with TensorFlow. Pneumonia, being a persistent and potentially hazardous lung infection, requires swift and correct analysis for proper care. Conventional diagnosis uses radiological imaging, which takes time and may lead to inconsistent results due to human interpretation. To address this, Deep Lung utilizes CNNs trained on extensive collections of chest X-rays to autonomously identify pneumonia. Leveraging TensorFlow’s dependable platform, the system reaches high levels of sensitivity and specificity, offering rapid clinical support to healthcare workers. This progressive application of deep learning in radiology signals a milestone in diagnostic accuracy, potentially minimizing medical expenses and elevating patient treatment outcomes.

Keywords : Deep Lung, Pneumonia Diagnosis, Convolutional Neural Networks, Cnns, Tensorflow, Chest X-Ray Analysis, Automated Detection, Radiological Imaging, High Sensitivity, High Specificity, Diagnostic Accuracy, Clinical Decision Support, Deep Learning In Healthcare, Medical Image Analysis, Reduced Medical Costs, Improved Patient Outcomes.

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