by Juiria Humayan, Md. Najmus Sakib Nahid, Amir Sohel, Md Alamgir Kabir, Md Shakhawat Hossain, Zahid Ullah, Mona Jamjoom The diagnosis of lung diseases such as pneumonia and tuberculosis remains a major global health challenge, especially in resource-limited regions.
Artificial Intelligence (AI) has shown strong potential in analyzing Chest X-Rays (CXR) for accurate and timely diagnosis, but most existing models are computationally heavy and lack interpretability, limiting their practical application. In this study, we present LXNet, a lightweight and explainable Convolutional Neural Network (CNN) for nine-class lung disease classification ( Normal , Pneumonia , Higher Density , Lower Density , Obstructive Pulmonary Diseases , Degenerative Infectious Diseases , Encapsulated Lesions , Mediastinal Changes and Chest Changes ).
The model was evaluated on a diverse CXR dataset containing 6,743 images collected from a private imaging center (GRS Imagem, Brazil), enabling comprehensive multiclass assessment. LXNet contains only 0.35 million parameters and employs a no-pooling final block to preserve subtle diagnostic features while maintaining very low computational cost.
PLOS ONE (Medicine) published a clinical update in Research Highlights on 17 Jun 2026.
The item focuses on LXNet: A lightweight CNN for lung disease classification from Chest X-ray with XAI-based interpretability.
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