lung-cancer-detection-efficientnetb0

Lung Cancer Detection using EfficientNet-B0

A research-grade deep learning model for lung cancer CT slice classification using the IQ-OTH/NCCD dataset.

Overview

This model classifies CT slices into three categories:

• Benign
• Malignant
• Normal

The model was trained using transfer learning with EfficientNet-B0 and achieved strong performance on the IQ-OTH/NCCD benchmark dataset.

Performance

Model Details

• Architecture: EfficientNet-B0
• Framework: PyTorch
• Training Strategy: Transfer Learning
• Input Size: 224 × 224
• Classes: 3
• Checkpoint Format: Full PyTorch checkpoint

The uploaded checkpoint contains:

• model_name
• state_dict
• class_names
• config

Features

• Lung cancer CT slice classification
• Transfer learning with ImageNet pretrained weights
• Explainability support through Grad-CAM
• Compatible with a Streamlit clinical-style dashboard
• Research-focused workflow and benchmarking pipeline

Dataset

Trained on the IQ-OTH/NCCD Lung Cancer Dataset.

Classes:

• Benign
• Malignant
• Normal

Limitations

• Slice-level classification only
• Uses JPG CT slices rather than DICOM images
• Not intended for clinical diagnosis
• Research and educational use only

License

Please follow the original dataset license and usage restrictions.

Citation

If you use this model, please cite the original IQ-OTH/NCCD dataset and associated research publications.