Lung cancer has become an increasingly prevalent disease, with an estimated 125,070 deaths in the United States alone in 2024 (5). To improve patient outcomes and assist doctors in differentiating between benign and malignant pulmonary nodules, this paper developed a Convolutional Neural Network (CNN) model for early binary detection of pulmonary nodules and assessed its effectiveness compared to other approaches. The CNN model showed an accuracy of 98.47%, while the radiomics-based SVM-LASSO model and the Lung-RADS system showed accuracies of 84.6% and 72.2% respectively. This demonstrates that the CNN model is significantly more effective for the early binary detection of pulmonary nodules than both the radiomics-based model and the Lung-RADS system. The paper also discusses the applications of Deep Learning in healthcare, concluding that although AI proves to be an effective method for early lung cancer detection, more research is needed to carefully assess the role and impact of AI in healthcare.

The authors have declared no competing interest.

This study did not receive any funding during the entire course of the research.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The source data i.e. the datasets used for training and testing of the models were openly available before the initiation of the study and can be located by the following links: Large COVID-19 CT Slice Dataset, Available: https://www.kaggle.com/datasets/maedemaftouni/large-covid19-ct-slice-dataset IQOTHNCCD - Lung Cancer Dataset, Available: https://www.kaggle.com/datasets/adityamahimkar/iqothnccd-lung-cancer-dataset, Accessed: Feb. 25, 2024. The Cancer Imaging Archive, Lung-PET-CT-Dx Collection, Available: https://www.cancerimagingarchive.net/collection/lung-pet-ct-dx/, Accessed: Feb. 26, 2024. The Cancer Imaging Archive, RIDER Lung CT Collection, Available: https://www.cancerimagingarchive.net/collection/rider-lung-ct/, Accessed: Jun. 18, 2024. The Cancer Imaging Archive, COVID-19 NY-SBU Collection (Normal), Available: https://www.cancerimagingarchive.net/collection/covid-19-ny-sbu/, Accessed: Jun. 20, 2024. The Cancer Imaging Archive, LIDC-IDRI (Cancerous), Available: https://www.cancerimagingarchive.net/collection/lidc-idri/, Accessed: Jun. 20, 2024

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes