The comparison of the recent research outcome with the proposed method
| Study/Author | Model/Approach | Domain | Key contributions | Limitations |
|---|---|---|---|---|
| Yang et al. (2024) [31] | Inception v4 | Diabetic retinopathy | High diagnostic accuracy using deep CNN; adaptable to lung imaging. | Not directly tested on lung cancer datasets. |
| Uddin et al. (2024) [32] | Multimodal learning | Lung cancer | Combines imaging with genetic and clinical data for enhanced precision. | Requires access to multiple modalities; complex data integration. |
| Nabeel et al. (2024) [33] | CNN with hyperparameter optimization | Lung cancer classification | Improved accuracy via fine-tuning CNN parameters. | Focuses only on classification; no segmentation or explainability features. |
| Yang et al. (2023) [34] | Bayesian inference + GLCM texture features | MRI-based cancer detection | Combines probabilistic models with handcrafted features for improved prediction. | Primarily based on MRI; lacks deep learning integration. |
| Prashanthi and Angelin Claret (2024) [27] | U-Net + Custom CNN | Lung nodule detection (CT images) | Integrated segmentation and classification with 98.3% accuracy; scalable, efficient for clinical use. | Lack of multi-class subtype differentiation. |
| Proposed Methodology | U-Net + Transfer learning (VGG-16, Inception v3, EfficientNet B0) | Lung nodule classification (CT biopsies) | Enhanced internal features, precise segmentation, and classification using EfficientNet B0 with 99.3% accuracy. Offers computational efficiency and early detection support for clinicians. | Focuses on binary classification; future work needed on subtype differentiation and XAI integration for better clinical practise. |
CNN: convolutional neural network