emergentai/cancer-resnet50-dual-input

Model Card: Model Card: Dual-Input ResNet50 for Cervical Cancer Image Classification

This model uses both Pre- and Post-stained cervical images as input and employs ResNet50 backbones with attention for feature fusion. It performs binary classification (Negative vs Positive).

Model Details

• Developed by: Beijuka / Pathogen Lab
• Funded by: STI
• Model type: Convolutional Neural Network (CNN)
• Input type: images (244x244)
• Output type: Binary classification (Negative, Positive)
• License: MIT
• Finetuned from: microsoft/resnet-50

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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.

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Bias, Risks, and Limitations

• The dataset was unbalance (class distribution imbalance).
• Model performance varies by threshold (see below).
• Limited dataset size means results may not generalize.

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.

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How to Get Started

from huggingface_hub import hf_hub_download
from tensorflow import keras

model_path = hf_hub_download(
    "Beijuka/cancer-resnet50-dual-input",
    "best_resnet50_prepost.h5"
)
model = keras.models.load_model(model_path)

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Training Details

Training Data

• Dual histopathology images (Pre-stained and Post-stained) of cervical cancer (RGB).

  ** Preprocessing:

  ** Resize to 224x224

  ** Normalize using tensorflow.keras.applications.resnet50.preprocess_input

  ** Augment training data with horizontal flips, brightness/contrast, rotation, and shift/scale/rotate transformations.

Training Procedure

• Optimizer: Adam
• Loss: Binary Crossentropy
• Batch size: 16 (initial), 8 (fine-tuning)
• Learning rate: 1e-4 (initial), 1e-5 (fine-tuning)
• Epochs: 30 (initial), 20 (fine-tuning)
• Callbacks: EarlyStopping, ModelCheckpoint, ReduceLROnPlateau

Data Splits (70:20:10)

• all splits contain both pre- and post-stained images

Split	Total	Negative	Positive
Training	338	304	34
Validation	96	86	10
Test	49	45	4

Hardware

• GPU: Tesla T4 (14GB)
• CUDA Version: 12.4
• Software: TensorFlow/Keras

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Evaluation

Testing Data

• Independent test set: 49 images (45 Negative, 4 Positive)

• Input: dual images (pre-stained and post-stained)

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Metrics at Threshold 0.5

Classification Report

              precision    recall  f1-score   support
Negative       0.94      0.95      0.95       309
Positive       0.44      0.38      0.41        29

accuracy                           0.91       338
macro avg       0.69      0.67      0.68       338
weighted avg    0.90      0.91      0.90       338

Overall Metrics

• Accuracy: 0.91
• Precision (Positive class): 0.44
• Recall (Positive class): 0.3793
• F1-Score (Positive class): 0.41

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Confusion Matrix

[[45, 0],
 [ 3, 1]]

Sensitivity / Specificity

• Sensitivity (Positive) = 0.25

• Specificity (Negative) = 1.00

Threshold Analysis

• Lower thresholds (≤0.35) favor recall → more positives detected but many false positives.
• Mid-range thresholds (0.40–0.50) balance accuracy and F1.
• Higher thresholds (>0.65) → model collapses to predicting only one class

Threshold	Accuracy	Precision	Recall	F1
0.00	0.0816	0.0816	1.0000	0.1509
0.05	0.0816	0.0816	1.0000	0.1509
0.10	0.1020	0.0833	1.0000	0.1538
0.15	0.1837	0.0909	1.0000	0.1667
0.20	0.2245	0.0952	1.0000	0.1739
0.25	0.4286	0.1000	0.7500	0.1765
0.30	0.5918	0.1364	0.7500	0.2308
0.35	0.7347	0.2000	0.7500	0.3158
0.40	0.8367	0.3000	0.7500	0.4286
0.45	0.8980	0.4000	0.5000	0.4444
0.50	0.9388	1.0000	0.2500	0.4000
0.55	0.9388	1.0000	0.2500	0.4000
0.60	0.9184	0.0000	0.0000	0.0000
0.65	0.9184	0.0000	0.0000	0.0000
0.70	0.9184	0.0000	0.0000	0.0000
0.75	0.9184	0.0000	0.0000	0.0000
0.80	0.9184	0.0000	0.0000	0.0000
0.85	0.9184	0.0000	0.0000	0.0000
0.90	0.9184	0.0000	0.0000	0.0000
0.95	0.9184	0.0000	0.0000	0.0000
1.00	0.9184	0.0000	0.0000	0.0000

Technical Specifications

Model Architecture

• Dual ResNet50 backbones for pre- and post-stained images

• Multi-Head Attention to fuse pre/post features

• Final Dense layer with sigmoid activation for binary output

• Trained using TensorFlow/Keras, with attention-based feature fusion

Compute Infrastructure

• Hardware: Tesla T4 GPU
• Software: TensorFlow/Keras

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