An Attention-Enhanced CNN Model for Early Lung Cancer Detection Using CT Imaging

Ashitha M M; Jaikrishnan M O; Jithinkrishnan P G; Usman Aijaz

doi:10.36948/ijfmr.2026.v08i03.78009

An Attention-Enhanced CNN Model for Early Lung Cancer Detection Using CT Imaging

Author(s)	Ms. Ashitha M M, Mr. Jaikrishnan M O, Mr. Jithinkrishnan P G, Dr. Usman Aijaz
Country	India
Abstract	In this paper, we provide an in-depth analysis of automatic lung cancer detection using deep learning models applied to the computed tomography (CT) images. The problem is extremely relevant because lung cancer continues being one of the most ethal cancers globally. Moreover, the disease is often diagnosed at a late stage and requires complex procedures for diagnosis. We suggest an attention-enhanced convolutional neural network (AECNN) methodology aimed at improving the diagnostic accuracy and lowering false negative ratios. Our approach combines con volutional feature extraction with the spatial attention module to prioritize the region of interest in CT images. The methodology was tested on the available databases where pre-processing steps were performed. The results showed that the proposed AE-CNN method allows attaining the accuracy level of 93.4and F1-score were significantly higher than in the cases of baseline CNN models and other machine learning algorithms used for the problem under discussion. Moreover, the research covers model’s robustness and scalability aspects.
Keywords	Deep learning, lung cancer detection, convolutional neural networks, attention mechanism, medical imaging, computed tomography. The problem is extremely relevant because lung cancer continues being one of the most lethal cancers globally
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-11
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.78009

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An Attention-Enhanced CNN Model for Early Lung Cancer Detection Using CT Imaging

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