Model Card: EfficientNet-B7 for Cervical Cancer Image Classification

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

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

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

Comparison of performance on Pre vs Post-stained images

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