Authors : S A Sabbirul Mohosin Naim; Tanvir Mahmud; Md Hossain

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/34vpczra

Scribd : https://tinyurl.com/yeykh7uf

DOI : https://doi.org/10.38124/ijisrt/25dec139

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Abstract : This study proposes Smart-LungNet, an automated deep learning framework designed to classify lung conditions into three categories: Normal, Lung Opacity, and Viral Pneumonia. Utilizing the Lung X-Ray Image Dataset of 3,475 images, we evaluated several pre-trained architectures, including ResNet18, DenseNet121, and MobileNetV2. MobileNetV2 was selected as the baseline due to its balance of efficiency and performance (88.5% accuracy). We enhanced this model by unfreezing all layers for fine-tuning and integrating a Squeeze-and-Excitation (SE) block after the initial convolutional layer to improve channel-wise feature attention. The proposed Smart-LungNet achieved a testing accuracy of 89.85% and an F1- score of 89.84%, outperforming ResNet18, DenseNet 121 and MobileNetV2. So, Smart-LungNet can help effectively to aid radiologists in the timely diagnosis of lung pathologies.

Keywords : Lung Disease, Computer Vision, Deep Learning, Medical Image Classification, Transfer Learning, Attention Module.

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