Authors : Ashish Kumar Mishra; Kakita Murali Gopal; Manish Prajapati; Shuvam Das; Om Prakash Das; N. Subham Rao

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/4eypuepk

Scribd : https://tinyurl.com/bdpjnn5y

DOI : https://doi.org/10.38124/ijisrt/26feb197

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

Abstract : Pneumonia remains the most serious health menace in the world, particularly to children below the age of five. Early and correct diagnosis is important in minimizing morbidity and mortality levels. Chest X-ray (CXR) has been considered as one of the key diagnostic tools that offer invaluable information about the pulmonary abnormalities, like infiltrates and opacities. Nevertheless, manual review of CXR scans tends to be affected by inter-observer conditions and diagnostic lags, and inconsistencies due to environmental and staffing conditions. Recent advances in the domain of deep learning and Convolutional Neural Networks (CNNs) have offered a good fit to the problem of automation of pneumonia detection in CXR images. In this paper, the study of the use of ConvNeXt is described in detail and is compared and contrasted with classical CNN models, including AlexNet, VGG16, and ResNet50. Transfer learning was used to fine- tune five variants of ConvNeXt in order to classify pediatric CXRs as pneumonia or normal images. The ConvNeXt-Large model reached an unprecedented accuracy of 98.66 and exceeded its smaller counterparts and all the classical CNN models. The findings prove that the current CNN frameworks with transformer inspired design concepts can substantially increase the attribute extraction properties and the generalization perfor- mance. The fact that ConvNeXt has the potential to reduce the instances of misclassification is further supported by confusion matrix analysis. The results highlight the significance of transfer learning and larger and modern architecture in medical image classification. ConvNeXt-based models demonstrate good promise as effective and dependable clinical decision-support systems, and they can be used to help radiologists and help optimize diagnostic processes—especially where resources are limited. The paper ends with the research directions for the future, consisting of hybrid architecture, multimodal learning, and explainable AI to enhance trust and interpretability in the clinical field.

Keywords : ConvNeXt, Pneumonia Detection, Adaptive Deep Learning, Deep Convolutional Neural Network Architecture.

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