An End-To-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques

Authors

Fariha Haque, Rajshahi University of Engineering and Technology
Mohammad Asif Hasan, Rajshahi University of Engineering and Technology
Md. Abu Ismali Siddique, Rajshahi University of Engineering and Technology
Tonmoy Roy, Utah State UniversityFollow
Tonmoy Kanti Shaha, Chittagong University of Engineering and Technology
Yamina Islam, International Islamic University Chittagong
Avijit Paul, Rajshahi University of Engineering and Technology
Muhammad E. H. Chowdhury, Qatar UniversityFollow

Document Type

Article

Journal/Book Title/Conference

IEEE Access

Author ORCID Identifier

Fariha Haque https://orcid.org/0009-0000-8886-4943

Mohammad Asif Hasan https://orcid.org/0009-0003-6447-2541

Tonmoy Roy https://orcid.org/0000-0002-0757-5523

Yamina Islam https://orcid.org/0009-0001-2220-8588

Avijit Paul https://orcid.org/0009-0005-7949-8482

Muhammad E. H. Chowdhury https://orcid.org/0000-0003-0744-8206

Volume

13

Publisher

Institute of Electrical and Electronics Engineers

Publication Date

5-21-2025

Journal Article Version

Version of Record

First Page

96317

Last Page

96336

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Abstract

In the field of medical imaging, deep learning (DL) techniques have made significant contributions to the detection and classification of various cancers. Identifying the precise regions in medical images containing cancerous cells plays a crucial role in the diagnostic process. Early and accurate cancer detection is essential for effective treatment and improved patient outcomes. However, manual diagnosis is labor-intensive, requiring the specialized expertise of radiologists, and the increasing number of cancer cases presents challenges in processing large volumes of image data efficiently. To address these challenges, an end-to-end concatenated Convolutional Neural Network (CNN) attention model has been proposed for automatic lung cancer classification. This approach integrates two distinct CNNs, followed by a multi-layer perceptron (MLP) and a multi-head attention (MHA) mechanism, to enhance performance. The model leverages explainable AI techniques, such as gradient-weighted class activation mapping (grad-CAM) and Shapley additive explanations (SHAP), to highlight critical regions within the images that influence the decision-making process. This model achieves impressive performance, with an accuracy of 99.54%, precision of 99.31%, recall of 99.95%, F1-score of 99.66%, and an AUC of 99.97%. These results demonstrate that this approach not only surpasses existing methods but also provides a highly accurate and interpretable solution. By reducing the need for extensive manual intervention, this model enables faster and more reliable lung cancer diagnosis, paving the way for timely and effective treatments.

Recommended Citation

Haque, F., Hasan, M.A., Siddique, M.A.I., et al. An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques. IEEE Access 2025 13.