{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:39:15Z","timestamp":1760060355291,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62471113","2024NS-FSC0479"],"award-info":[{"award-number":["62471113","2024NS-FSC0479"]}]},{"name":"Sichuan Science and Technology Program","award":["62471113","2024NS-FSC0479"],"award-info":[{"award-number":["62471113","2024NS-FSC0479"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Lung cancer is one of the leading causes of cancer-related mortality worldwide. The diagnosis of this disease remains a challenge due to the subtle and ambiguous nature of early-stage symptoms and imaging findings. Deep learning approaches, specifically Convolutional Neural Networks (CNNs), have significantly advanced medical image analysis. However, conventional architectures such as ResNet50 that rely on first-order pooling often fall short. This study aims to overcome the limitations of CNNs in lung cancer classification by proposing a novel and dynamic model named LungSE-SOP. The model is based on Second-Order Pooling (SOP) and Squeeze-and-Excitation Networks (SENet) within a ResNet50 backbone to improve feature representation and class separation. A novel Dynamic Feature Enhancement (DFE) module is also introduced, which dynamically adjusts the flow of information through SOP and SENet blocks based on learned importance scores. The model was trained using a publicly available IQ-OTH\/NCCD lung cancer dataset. The performance of the model was assessed using various metrics, including the accuracy, precision, recall, F1-score, ROC curves, and confidence intervals. For multiclass tumor classification, our model achieved 98.6% accuracy for benign, 98.7% for malignant, and 99.9% for normal cases. Corresponding F1-scores were 99.2%, 99.8%, and 99.9%, respectively, reflecting the model\u2019s high precision and recall across all tumor types and its strong potential for clinical deployment.<\/jats:p>","DOI":"10.3390\/jimaging11080283","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T15:19:02Z","timestamp":1755789542000},"page":"283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic\u2013Attentive Pooling Networks: A Hybrid Lightweight Deep Model for Lung Cancer Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1982-3625","authenticated-orcid":false,"given":"Williams","family":"Ayivi","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Department of Information Systems and Operations Management, Vienna University of Economics and Business, 1020 Vienna, Austria"}]},{"given":"Xiaoling","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Wisdom Xornam","family":"Ativi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Francis","family":"Sam","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}]},{"given":"Franck A. P.","family":"Kouassi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kumaran, S.Y., Jeya, J.J., Mahesh, T.R., Khan, S.B., Alzahrani, S., and Alojail, M. (2024). Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam. BMC Med. Imaging, 24.","DOI":"10.1186\/s12880-024-01345-x"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tian, L., Wu, J., Song, W., Hong, Q., Liu, D., Ye, F., Gao, F., Hu, Y., Wu, M., and Lan, Y. (2024). Precise and automated lung cancer cell classification using deep neural network with multiscale features and model distillation. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-61101-7"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Najjar, R. (2023). Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics, 13.","DOI":"10.20944\/preprints202306.1124.v1"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Causey, J.L., Zhang, J., Ma, S., Jiang, B., Qualls, J.A., Politte, D.G., Prior, F., Zhang, S., and Huang, X. (2018). Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-27569-w"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3881","DOI":"10.1007\/s10115-023-01894-7","article-title":"Computer-aided diagnosis systems: A comparative study of classical machine learning versus deep learning-based approaches","volume":"65","author":"Guetari","year":"2023","journal-title":"Knowl. Inf. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis","volume":"4","author":"Aggarwal","year":"2021","journal-title":"npj Digit. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3790617","DOI":"10.1155\/2024\/3790617","article-title":"Evaluating CNN Architectures and Hyperparameter Tuning for Enhanced Lung Cancer Detection Using Transfer Learning","volume":"2024","author":"Ansari","year":"2024","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamar\u00eda, J., Fadhel, M.A., Al-Amidie, M., and Farhan, L. (2021). Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions, Springer International Publishing.","DOI":"10.1186\/s40537-021-00444-8"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100316","DOI":"10.1016\/j.health.2024.100316","article-title":"A novel Deep Learning architecture for lung cancer detection and diagnosis from Computed Tomography image analysis","volume":"5","author":"Crasta","year":"2024","journal-title":"Healthc. Anal."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3866","DOI":"10.1016\/j.neuron.2022.09.012","article-title":"Challenges for machine learning in clinical translation of big data imaging studies","volume":"110","author":"Dinsdale","year":"2022","journal-title":"Neuron"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tao, Z., Xiaoyu, C., Huiling, L., Xinyu, Y., Yuncan, L., and Xiaomin, Z. (2022). Pooling Operations in Deep Learning: From \u2018Invariable\u2019 to \u2018Variable\u2019. Biomed Res. Int., 2022.","DOI":"10.1155\/2022\/4067581"},{"key":"ref_12","first-page":"430","article-title":"Semantic segmentation with second-order pooling","volume":"Volume 7578 LNCS","author":"Carreira","year":"2012","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioin-Formatics)"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.1177\/14759217211053776","article-title":"Efficient attention-based deep encoder and decoder for automatic crack segmentation","volume":"21","author":"Kang","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_15","first-page":"1","article-title":"Comparative analysis of deep learning techniques for lung cancer identification","volume":"3232","author":"Abdulghafoor","year":"2024","journal-title":"AIP Conf. Proc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Korkmaz, A.F., Ekinci, F., Alta\u015f, \u015e., Kumru, E., G\u00fczel, M.S., and Akata, I. (2025). A Deep Learning and Explainable AI-Based Approach for the Classification of Discomycetes Species. Biology, 14.","DOI":"10.3390\/biology14060719"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kumru, E., Ugurlu, G., Sevindik, M., Ekinci, F., G\u00fczel, M.S., Acici, K., and Akata, I. (2025). Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer Architectures. Biology, 14.","DOI":"10.3390\/biology14070816"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zafar, A., Saba, N., Arshad, A., Alabrah, A., Riaz, S., Suleman, M., Zafar, S., and Nadeem, M. (2024). Convolutional Neural Networks: A Comprehensive Evaluation and Benchmarking of Pooling Layer Variants. Symmetry, 16.","DOI":"10.3390\/sym16111516"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111735","DOI":"10.1016\/j.asoc.2024.111735","article-title":"Squeeze-and-excitation 3D convolutional attention recurrent network for end-to-end speech emotion recognition","volume":"161","author":"Saleem","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hu, J. (2025, August 16). Squeeze-and-Excitation_Networks_CVPR_2018_paper.pdf. Available online: http:\/\/openaccess.thecvf.com\/content_cvpr_2018\/html\/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_22","first-page":"2965","article-title":"Matrix backpropagation for deep networks with structured layers","volume":"2015","author":"Ionescu","year":"2015","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TPAMI.2017.2723400","article-title":"Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition","volume":"40","author":"Lin","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","unstructured":"Alyasriy, H., and AL-Huseiny, M. (2025, August 16). The IQ-OTH\/NCCD Lung Cancer Dataset. Mendeley Data 2023, V4. Available online: https:\/\/data.mendeley.com\/datasets\/bhmdr45bh2\/4."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Feng, W., Wu, Z., Li, W., Tao, L., Liu, X., Zhang, F., Gao, Y., Huang, J., and Guo, X. (2023). Deep-Learning Model of ResNet Combined with CBAM for Malignant\u2013Benign Pulmonary Nodules Classification on Computed Tomography Images. Medicina, 59.","DOI":"10.3390\/medicina59061088"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108309","DOI":"10.1016\/j.patcog.2021.108309","article-title":"ProCAN: Progressive growing channel attentive non-local network for lung nodule classification","volume":"122","author":"Shak","year":"2022","journal-title":"Pattern Recognit."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/283\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:32:45Z","timestamp":1760034765000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/283"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,21]]},"references-count":26,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["jimaging11080283"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11080283","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2025,8,21]]}}}