Table 9.
Comparative analysis of lung cancer prediction through deep learning
| Aspect | Implementation Platform | Dataset Details |
|---|---|---|
| Platform Used | Deep learning models: ResNet-50, ResNet-101, EfficientNet-B3 | LIDC-IDRI repository |
| Input Data | DICOM lung cancer images | 1,000 images |
| Data Partitioning | Training: 70% Validation: 20% Testing: 10% | Training: 613 images Validation: 315 images Testing: 72 images |
| Model Architecture | ResNet-50, ResNet-101, EfficientNet-B3 | – |
| Preprocessing Techniques | Data augmentation strategy | – |
| Classification Performance | Fusion Model: 100% precision in classifying Squamous Cells | Precision: ResNet-50, EfficientNet-B3, and ResNet-101 achieved 90%, followed by EfficientNet-B3 and ResNet-101 with slightly lower precision |
| Model Training | Epochs: 35 Batch Size: 32 | – |
| Learning Rate | Adam optimizer with a learning rate of 0.001 | – |
| Total Parameters | 10,988,787 | – |
| Trainable Parameters | 10,099,090 | – |
| Non-trainable Parameters | 889,697 | – |
| Achievements | Improved accuracy in predicting lung cancer subtypes | Potential for advancements in healthcare and reduction in mortality rates associated with lung cancer |