EffAtt-LungNet: An attention-guided EfficientNet framework for three-class lung histopathology classification

Muhammad Ahsan Jami, Ahsan and Malik Daler Ali Awan, Daler and Fatima Bukhari, Fatima and Malik Muhammad Saad Missen, Saad and Nadeem Iqbal Kajla, Nadeem (2026) EffAtt-LungNet: An attention-guided EfficientNet framework for three-class lung histopathology classification. Plos One, 21 (10). ISSN 1932-6203

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Abstract

This study presents EffAtt-LungNet, an attention-guided deep learning framework for three-class lung histopathology classification. The model combines an EfficientNetB3 backbone with the Convolutional Block Attention Module (CBAM) to refine features and improve discrimination among benign lung tissue, lung adenocarcinoma, and lung squamous cell carcinoma. Experiments used the publicly released LC25000 lung subset, which contains 15,000 images across three classes and was itself produced by augmenting a smaller collection of underlying histopathology images. We generated 10,500 additional fixed variants, creating a balanced working corpus of 25,500 images with training, validation, and test partitions of 15,300, 2,550, and 7,650 images. EffAtt-LungNet reached 99.86% accuracy, 99.86% precision, 99.86% recall, 99.86% F1 score, and 0.9997 AUC on the image-level test partition. Performance was stable across five random seeds (mean accuracy 99.86 ± 0.02%), and paired errors differed from those of the EfficientNetB3 backbone (McNemar’s test, χ2 = 47.27, p<0.001). Because pre-augmentation source identifiers for the released LC25000 images were unavailable, independence at the earliest source-image level could not be confirmed. The results therefore represent internal tile-level benchmark performance and require validation on independent, non-augmented, multi-centre datasets before any translational or clinical claim can be made.

Item Type:	Article
Subjects:	Computer Science > Computer Software
Research Centres:	Regulated Software Research Centre
Depositing User:	NadeemIqbal Kajla
Date Deposited:	22 Oct 2026 07:25
Last Modified:	22 Oct 2026 07:25
License:	Creative Commons: Attribution-Noncommercial-Share Alike 4.0
URI:	https://eprints.dkit.ie/id/eprint/1115

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