Lung Cancer Detection Efficientnetb0

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.

--- license: mit language: - en base_model: - google/efficientnet-b0 pipeline_tag: image-classification --- # 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 | Metric | Score | | ----------------- | ------ | | Accuracy | 97.59% | | Weighted F1 Score | 97.59% | | ROC-AUC | 99.63% | ## 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.