Table 1 Comprehensive overview of various lung cancer prediction models.

Nannapaneni, D. et al., 2023⁴⁷

CNN (3 conv, 2 max-pooling, 1 FC)

Detected lung cancer from histopathology images

Preprocessing improved image quality and analysis

×

×

×

×

Image classification (histopathology)

Limited comparison with advanced methods or interpretability

Shakeel, P.M. et al., 2022⁴⁸

Enhanced DNN, hybrid optimization, ensemble classifier

robust lung cancer detection

Feature selection and image enhancement improved efficiency

×

×

×

×

Multilevel brightness preservation, region segmentation

Lacks interpretability and real-world validation

Shen, Z. et al., 2023⁵⁰

WS-LungNet (semi-supervised DL)

Improved nodule detection and malignancy evaluation

Semi-supervised learning utilized unlabelled data

×

×

×

×

Nodule segmentation, malignancy prediction

Needs further validation and comparison with supervised methods

Xu, H., 2023⁵¹

CNN (ResNet-based, deeper architectures)

Improved accuracy in lung CT image classification

Addressed low learning efficiency using deeper networks

×

×

×

×

Classification of lung CT images

Limited privacy and interpretability

Chandran, U. et al., 2023⁵⁵

ML-based lung cancer risk prediction model

Identified a high-risk group with 9x higher lung cancer incidence

Scalable predictions using real-world EHR data

×

×

×

×

Risk prediction for high-risk groups using EHR and claims data

Lacks advanced privacy and interpretability mechanisms

Shaffie, A. et al., 2019⁶⁰

MGRF with 3D HOG, stacked auto-encoder

Feature extraction and fusion for lung cancer classification

Efficiently analyzed large datasets using fusion techniques

×

×

×

×

Diagnosis, prognosis, survival prediction (CT/MRI)

Lacks privacy-preserving techniques and interpretability

Agrawal, A. et al., 2011⁶¹

Combined voting classifier

Predicted Survival with 90% accuracy for 6 months, 1 Year, 5 years

Utilized the SEER dataset for efficient survival prediction

×

×

×

×

Survival prediction using SEER

Limited external validation and interpretability

Mohalder, R.D. et al., 2022⁶²

CNN (ReLU, softmax)

Detected abnormal tumor patterns in HPI

Effectively analyzed complex tumor data

×

×

×

×

Tumor pattern recognition in CRC (HPI)

Limited comparison with state-of-the-art methods, and lacks interpretability.

Su, Y. et al., 2022⁶³

WGCNA with Lasso, DT, RF, SVM

Detected colon cancer, staging via differential gene expression

Reduced complexity using Lasso and WGCNA

×

×

×

×

Gene-based classification and staging

Lack of privacy, collaborative learning, and interpretability

Garg, S. et al., 2020⁶⁴

Pre-trained CNNs (MobileNet, InceptionResNetV2, etc.)

Detected colon and lung cancer (HPI)

Enhanced efficiency with pre-trained models and augmentation

×

×

×

×

Cancer detection using histopathology

Lacks privacy, collaborative learning, and interpretability

Li, L. et al., 2019⁶⁵

DL-CAD system

Detected lung nodules (< 3 mm), malignancy prediction

Efficiently analyzed small nodules (LIDC-IDRI, NLST datasets)

×

×

×

×

Lung nodule detection and malignancy prediction

Moderate accuracy (86.2%), lacks privacy and interpretability

Teramoto, A. et al., 2017⁶⁶

Deep CNN (DCNN)

Automated classification of lung cancer with 71% accuracy

Small dataset (76 cases), limited computational demands

×

×

×

×

Lung cancer classification

Low accuracy, small dataset, lacks privacy, and interpretability.

Proposed interpretable global model

CNN, EfficientNetB0, InceptionV3, DenseNet121

High predictive accuracy (98.21%) in lung cancer diagnosis

Enhanced through MapReduce for distributed processing

✔

✔

✔

✔

Lung cancer prediction using aggregated and decentralized learning

Requires synchronization and computational resources