{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T10:24:19Z","timestamp":1787048659641,"version":"build-2736575974"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Lung cancer arises from mutations in lung cells, disrupting their normal growth cycle and leading to uncontrolled cell division. These rapidly dividing cells lose function and fail to form healthy lung tissue. Several factors contribute to the difficulty of diagnosing and classifying lung nodules, including the high degree of morphological heterogeneity and overlapping characteristics between benign and malignant nodules. Recently, deep learning models have been used in computed tomography (CT)-based lung nodule diagnosis and have demonstrated diagnostic efficiency comparable to that of radiologists. This study introduces LungCNET, a high-performance multi-layer deep convolutional neural network trained on chest CT images to improve lung lesion classification efficiency and accuracy significantly. The data for the Lung Cancer convolutional neural network (LungCNET) were derived from the IQ-OTH\/NCCD CT scan dataset (1097 images from 110 cases), split into training (767 images), validation (109) and a held-out test partition (221) that played no role in training or model selection. This dataset encompasses three diagnostic categories: benign, malignant, and normal lung tissues. LungCNET was evaluated against fine-tuned benchmark models that are both established and widely used, spanning architectures introduced between 2014 and 2024, including VGG16, ResNet50, InceptionV3, MobileNetV2, and YOLOv11. On the held-out test partition, LungCNET reached a macro-averaged F1-score of 95.19%, with VGG16 at 94.09% and InceptionV3 at 92.28%; these three models performed comparably, and the separation between them is small relative to the resolution of a test set of this size. LungCNET was, however, the only model to exceed 90% F1-score across all three diagnostic classes simultaneously, and recorded the highest F1-score on the benign class (91.0%), the smallest and most frequently misclassified category, where two of the six models failed entirely. These results support LungCNET as a candidate tool for lung cancer diagnosis, subject to validation on larger and independently sourced datasets.<\/jats:p>","DOI":"10.3390\/bioengineering13080931","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T09:06:41Z","timestamp":1787044001000},"page":"931","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LungCNET: A High-Performance Deep CNN Model for Lung Cancer Detection Evaluated Against Widely Used CNN Benchmarks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3129-2101","authenticated-orcid":false,"given":"Elham","family":"Eskandarnia","sequence":"first","affiliation":[{"name":"Faculty of Computer Studies, Arab Open University, Building 890, Street 3220, A\u2019ali 732, Bahrain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Adepoju","sequence":"additional","affiliation":[{"name":"Data Science and AI HUB, University of Salford, Manchester M5 4WT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4541-5907","authenticated-orcid":false,"given":"Kaveh","family":"Kiani","sequence":"additional","affiliation":[{"name":"Data Science and AI HUB, University of Salford, Manchester M5 4WT, UK"},{"name":"School of Science, Engineering & Environment, University of Salford, Manchester M5 4WT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1539-5546","authenticated-orcid":false,"given":"Taha","family":"Mansouri","sequence":"additional","affiliation":[{"name":"Data Science and AI HUB, University of Salford, Manchester M5 4WT, UK"},{"name":"School of Science, Engineering & Environment, University of Salford, Manchester M5 4WT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4896-4210","authenticated-orcid":false,"given":"Ayah","family":"Binrajab","sequence":"additional","affiliation":[{"name":"School of Medicine, Royal College of Surgeons in Ireland\u2013Medical University of Bahrain, Building 2441, Road 2835, Busaiteen 228, Bahrain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,18]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","unstructured":"World Health Organization (2026, July 02). Lung Cancer, Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/lung-cancer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1158\/1055-9965.EPI-19-0221","article-title":"Cancer Progress and Priorities: Lung Cancer","volume":"28","author":"Schabath","year":"2019","journal-title":"Cancer Epidemiol. Biomark. Prev."},{"key":"ref_4","first-page":"45","article-title":"Epidemiology of Lung Cancer","volume":"25","author":"Thandra","year":"2021","journal-title":"Contemp. Oncol."},{"key":"ref_5","unstructured":"LUNGevity Foundation (2026, July 02). Screening & Early Detection. Available online: https:\/\/www.lungevity.org\/lung-cancer-basics\/screening-early-detection."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/S0140-6736(03)14205-5","article-title":"Screening for Early Lung Cancer with Low-Dose Spiral Computed Tomography","volume":"362","author":"Diederich","year":"2003","journal-title":"Lancet"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.gpb.2022.11.003","article-title":"Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis","volume":"20","author":"Li","year":"2022","journal-title":"Genom. Proteom. Bioinform."},{"key":"ref_8","unstructured":"National Cancer Institute (2026, July 02). Early Detection Research\u2014Prevention, Available online: https:\/\/prevention.cancer.gov\/research-groups\/early-detection."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"508","DOI":"10.31128\/AJGP-11-19-5148","article-title":"Diagnosis and treatment of early lung cancer","volume":"49","author":"McLellan","year":"2020","journal-title":"Aust. J. Gen. Pract."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7184","DOI":"10.1007\/s00330-021-07800-5","article-title":"Interobserver Variability in Lung CT Screening Reporting and Data System Categorisation in Subsolid Nodule-Enriched Lung Cancer Screening CTs","volume":"31","author":"Yoon","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_11","first-page":"1","article-title":"Global Challenges for Cancer Imaging","volume":"4","author":"Schlemmer","year":"2018","journal-title":"J. Glob. Oncol."},{"key":"ref_12","first-page":"7","article-title":"Cancer Statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA Cancer J. Clin."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding Tumour Phenotype by Noninvasive Imaging Using a Quantitative Radiomics Approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_14","first-page":"454","article-title":"Computer-Aided Diagnosis of Lung Cancer with Multi-Type Feature Analysis Using Support Vector Machines","volume":"25","author":"Suzuki","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","first-page":"41","article-title":"Applications of Support Vector Machine (SVM) Learning in Cancer Genomics","volume":"15","author":"Huang","year":"2018","journal-title":"Cancer Genom. Proteom."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11769351231167992","DOI":"10.1177\/11769351231167992","article-title":"Novel Biomarker Prediction for Lung Cancer Using Random Forest Classifiers","volume":"22","author":"C","year":"2023","journal-title":"Cancer Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e13697","DOI":"10.1111\/exsy.13697","article-title":"Prospect of Large Language Models and Natural Language Processing for Lung Cancer Diagnosis: A Systematic Review","volume":"41","author":"Garg","year":"2024","journal-title":"Expert Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A Survey on Deep Learning in Medical Image Analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","article-title":"Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks","volume":"35","author":"Setio","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104284","DOI":"10.1016\/j.ijmedinf.2020.104284","article-title":"Improving the Performance of CNN to Predict the Likelihood of COVID-19 Using Chest X-Ray Images with Preprocessing Algorithms","volume":"144","author":"Heidari","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1118\/1.3528204","article-title":"The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans","volume":"38","author":"McLennan","year":"2011","journal-title":"Med. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"National Lung Screening Trial Research Team (2011). Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. N. Engl. J. Med., 365, 395\u2013409.","DOI":"10.1056\/NEJMoa1102873"},{"key":"ref_24","first-page":"1078","article-title":"Transfer Learning with GoogLeNet for the Detection of Lung Cancer","volume":"22","author":"Sajit","year":"2021","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100588","DOI":"10.1016\/j.measen.2022.100588","article-title":"Detection and Classification of Lung Cancer Using CNN and GoogleNet","volume":"24","author":"Pandian","year":"2022","journal-title":"Meas. Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4067832","DOI":"10.1155\/2017\/4067832","article-title":"Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks","volume":"2017","author":"Teramoto","year":"2017","journal-title":"BioMed Res. Int."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.lungcan.2021.01.027","article-title":"Lung Cancer Prediction by Deep Learning to Identify Benign Lung Nodules","volume":"154","author":"Heuvelmans","year":"2021","journal-title":"Lung Cancer"},{"key":"ref_28","unstructured":"Klyuev, E., and Evdokimova, V. (2023). Lung Tumor Detection and Recognition Using Deep Convolutional Neural Networks. Proceedings of the Computational Science and Its Applications\u2014ICCSA 2022 Workshops, Springer."},{"key":"ref_29","unstructured":"(2026, July 02). Transfer Learning Using VGG-16 with Deep Convolutional Neural Network for Classifying Images. Available online: https:\/\/www.ijsrp.org\/research-paper-1019.php?rp=P949194."},{"key":"ref_30","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015). Rethinking the Inception Architecture for Computer Vision. arXiv.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_32","unstructured":"Jocher, G., and Qiu, J. (2026, August 11). Ultralytics YOLO11, Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_33","unstructured":"Alyasriy, H., and AL-Huseiny, M. (2026, August 11). The IQ-OTHNCCD Lung Cancer Dataset (Version 2) [Dataset]. Mendeley Data, Available online: https:\/\/data.mendeley.com\/datasets\/bhmdr45bh2\/2."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/8\/931\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T09:26:09Z","timestamp":1787045169000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/8\/931"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,18]]},"references-count":33,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["bioengineering13080931"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13080931","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,18]]}}}