An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques
<p dir="ltr">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...
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2025
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| _version_ | 1864513533608198144 |
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| author | Fariha Haque (21485518) |
| author2 | Mohammad Asif Hasan (22303759) Md. Abu Ismail Siddique (22503755) Tonmoy Roy (21485527) Tonmoy Kanti Shaha (22503758) Yamina Islam (22303756) Avijit Paul (22303753) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author author author |
| author_facet | Fariha Haque (21485518) Mohammad Asif Hasan (22303759) Md. Abu Ismail Siddique (22503755) Tonmoy Roy (21485527) Tonmoy Kanti Shaha (22503758) Yamina Islam (22303756) Avijit Paul (22303753) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fariha Haque (21485518) Mohammad Asif Hasan (22303759) Md. Abu Ismail Siddique (22503755) Tonmoy Roy (21485527) Tonmoy Kanti Shaha (22503758) Yamina Islam (22303756) Avijit Paul (22303753) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2025-06-09T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3572423 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_End-to-End_Concatenated_CNN_Attention_Model_for_the_Classification_of_Lung_Cancer_With_XAI_Techniques/30455570 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence Lung cancer convolutional neural network CLAHE explainable AI Shapley additive explanation gradient-weighted class activation mapping Gradio Accuracy Computed tomography Feature extraction Tumors Transfer learning Medical diagnostic imaging Convolutional neural networks |
| dc.title.none.fl_str_mv | An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3572423" target="_blank">https://dx.doi.org/10.1109/access.2025.3572423</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4da2b150ad47d5deb0e37b48bae500e7 |
| identifier_str_mv | 10.1109/access.2025.3572423 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30455570 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI TechniquesFariha Haque (21485518)Mohammad Asif Hasan (22303759)Md. Abu Ismail Siddique (22503755)Tonmoy Roy (21485527)Tonmoy Kanti Shaha (22503758)Yamina Islam (22303756)Avijit Paul (22303753)Muhammad E. H. Chowdhury (14150526)Biomedical and clinical sciencesOncology and carcinogenesisHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceLung cancerconvolutional neural networkCLAHEexplainable AIShapley additive explanationgradient-weighted class activation mappingGradioAccuracyComputed tomographyFeature extractionTumorsTransfer learningMedical diagnostic imagingConvolutional neural networks<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3572423" target="_blank">https://dx.doi.org/10.1109/access.2025.3572423</a></p>2025-06-09T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3572423https://figshare.com/articles/journal_contribution/An_End-to-End_Concatenated_CNN_Attention_Model_for_the_Classification_of_Lung_Cancer_With_XAI_Techniques/30455570CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304555702025-06-09T06:00:00Z |
| spellingShingle | An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques Fariha Haque (21485518) Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence Lung cancer convolutional neural network CLAHE explainable AI Shapley additive explanation gradient-weighted class activation mapping Gradio Accuracy Computed tomography Feature extraction Tumors Transfer learning Medical diagnostic imaging Convolutional neural networks |
| status_str | publishedVersion |
| title | An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques |
| title_full | An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques |
| title_fullStr | An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques |
| title_full_unstemmed | An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques |
| title_short | An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques |
| title_sort | An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques |
| topic | Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence Lung cancer convolutional neural network CLAHE explainable AI Shapley additive explanation gradient-weighted class activation mapping Gradio Accuracy Computed tomography Feature extraction Tumors Transfer learning Medical diagnostic imaging Convolutional neural networks |