| --- |
| license: mit |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| base_model: |
| - google/efficientnet-b7 |
| pipeline_tag: image-classification |
| tags: |
| - medical |
| - cervical-cancer |
| - histopathology |
| - undersampling |
| --- |
| |
| # Model Card: EfficientNet-B7 for Cervical Cancer Image Classification |
|
|
| This model fine-tunes **EfficientNet-B7** for the task of binary cervical cancer image classification (Negative vs. Positive). It was trained using undersampling to handle class imbalance. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| - **Developed by:** Beijuka / Pathogen Lab |
| - **Funded by:** STI |
| - **Model type:** Convolutional Neural Network (CNN) |
| - **Input type:** Histopathology images (600x600, RGB) |
| - **Output type:** Binary classification (Negative, Positive) |
| - **License:** MIT |
| - **Finetuned from:** `google/efficientnet-b7` |
|
|
| <!-- ### Model Sources |
| - **Repository:** [Your HF Repo URL] |
| - **Paper [optional]:** [If you want to link e.g., EfficientNet or related research] |
| - **Demo [optional]:** [Streamlit/Gradio app if you plan one] |
| --> |
| --- |
|
|
| ## Uses |
|
|
| ### Direct Use |
| - classification of cervical cancer images into Negative vs Positive cases. |
|
|
| ### Downstream Use |
| - Could be integrated into diagnostic support pipelines. |
| - Adapted for related medical imaging classification tasks. |
|
|
| ### Out-of-Scope Use |
| - **Not** a replacement for professional medical diagnosis. |
| - Should not be deployed clinically without regulatory approval. |
| - Not suitable for non-cervical images. |
|
|
| --- |
|
|
| ## Bias, Risks, and Limitations |
|
|
| - The dataset was undersampled → may affect generalizability. |
| - Model performance varies by threshold (see below). |
| - Limited dataset size (19 test images) means results may not generalize. |
| - Potential domain shift if applied to different staining/preparation protocols. |
|
|
| ### Recommendations |
| - Validate on larger, more diverse datasets. |
| - Carefully calibrate decision threshold depending on application (screening vs confirmatory). |
| - Use alongside clinical expertise, not as a standalone tool. |
|
|
| --- |
|
|
| ## How to Get Started |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| from tensorflow import keras |
| |
| model_path = hf_hub_download( |
| "Beijuka/cancer-efficientnetb7-undersampling", |
| "cancer_efficientnetB7_undersampling.keras" |
| ) |
| model = keras.models.load_model(model_path) |
| ```` |
|
|
| --- |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| * Histopathology images of cervical cancer (size 600x600, RGB). |
| * Class imbalance addressed via **undersampling**: |
|
|
| * Positive: 84 images |
| * Negative: 100 images |
| * Preprocessing: Normalization + resizing. |
|
|
| ### Training Procedure |
|
|
| * Optimizer: Adam |
| * Loss: Binary Crossentropy |
| * Batch size: 8 |
| * Learning rate: 1e-3 (initial), 1e-5 (fine-tuning) |
| * Epochs: 50 (initial), 20 (fine-tuning) |
| * EarlyStopping and ModelCheckpoint callbacks used. |
|
|
| ### Data Splits (70:20:10) |
|
|
| * **Training:** 128 images (70 Negative, 29 Positive Post-stained, 29 Positive Pre-stained) |
| * **Validation:** 37 images (20 Negative, 8 Positive Post-stained, 9 Positive Pre-stained) |
| * **Test:** 19 images (10 Negative, 5 Positive Post-stained, 4 Positive Pre-stained) |
|
|
| ### Hardware |
|
|
| * GPU: Tesla T4 (14GB) |
| * CUDA Version: 12.4 |
| * Software: TensorFlow/Keras |
|
|
| --- |
|
|
| ## Evaluation |
|
|
| ### Testing Data |
|
|
| * Independent test set: 19 images (10 Negative, 9 Positive) |
|
|
| ### Metrics at Threshold 0.5 |
|
|
| * **Accuracy:** 0.7368 |
| * **Precision (Positive):** 0.8333 |
| * **Recall (Positive):** 0.5556 |
| * **F1-Score (Positive):** 0.6667 |
|
|
| #### Confusion Matrix |
|
|
| ``` |
| [[9, 1], |
| [4, 5]] |
| ``` |
|
|
| #### Sensitivity / Specificity |
|
|
| * Negative: Sensitivity 0.90, Specificity 0.56 |
| * Positive: Sensitivity 0.56, Specificity 0.90 |
|
|
| ### Threshold Analysis |
|
|
| * Best balance observed near 0.45–0.50 |
| * Lower thresholds → higher recall, more false positives |
| * Higher thresholds (>0.65) → model collapses to predicting only one class |
|
|
| | Threshold | Accuracy | Precision | Recall | F1 | |
| | --------- | -------- | --------- | ------ | ------ | |
| | 0.00 | 0.4737 | 0.4737 | 1.0000 | 0.6429 | |
| | 0.05 | 0.4737 | 0.4737 | 1.0000 | 0.6429 | |
| | 0.10 | 0.5263 | 0.5000 | 1.0000 | 0.6667 | |
| | 0.15 | 0.5263 | 0.5000 | 0.8889 | 0.6400 | |
| | 0.20 | 0.6316 | 0.5714 | 0.8889 | 0.6957 | |
| | 0.25 | 0.6316 | 0.5833 | 0.7778 | 0.6667 | |
| | 0.30 | 0.6316 | 0.6250 | 0.5556 | 0.5882 | |
| | 0.35 | 0.6316 | 0.6250 | 0.5556 | 0.5882 | |
| | 0.40 | 0.6842 | 0.7143 | 0.5556 | 0.6250 | |
| | 0.45 | 0.7368 | 0.8333 | 0.5556 | 0.6667 | |
| | 0.50 | 0.7368 | 0.8333 | 0.5556 | 0.6667 | |
| | 0.55 | 0.6842 | 0.8000 | 0.4444 | 0.5714 | |
| | 0.60 | 0.6842 | 1.0000 | 0.3333 | 0.5000 | |
| | 0.65 | 0.5263 | 0.0000 | 0.0000 | 0.0000 | |
| | 0.70 | 0.5263 | 0.0000 | 0.0000 | 0.0000 | |
| | 0.75 | 0.5263 | 0.0000 | 0.0000 | 0.0000 | |
| | 0.80 | 0.5263 | 0.0000 | 0.0000 | 0.0000 | |
| | 0.85 | 0.5263 | 0.0000 | 0.0000 | 0.0000 | |
| | 0.90 | 0.5263 | 0.0000 | 0.0000 | 0.0000 | |
| | 0.95 | 0.5263 | 0.0000 | 0.0000 | 0.0000 | |
|
|
|
|
| ### Comparison of performance on Pre vs Post-stained images |
|
|
| | Comparison | Accuracy | F1-Score | Precision |Recall | |
| | ------------------------------ | -------- | -------- | -------- | ------- | |
| | Pre-stained Prediction | 0.6087 | 0.2703 |0.1613 |0.8333| |
| | Post-stained Prediction | 0.7474 | 0.3441 |0.2222 |0.7619| |
|
|
| --- |
|
|
| ## Technical Specifications |
|
|
| ### Model Architecture |
|
|
| * EfficientNet-B7 backbone |
| * Final Dense layer with sigmoid activation for binary classification |
|
|
| ### Compute Infrastructure |
|
|
| * **Hardware:** Tesla T4 GPU |
| * **Software:** TensorFlow/Keras |
|
|
| --- |
|
|
