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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- image-classification |
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- medical |
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- cervical-cancer |
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- pytorch |
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- cnn |
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- colposcopy |
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datasets: |
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- custom |
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metrics: |
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- accuracy |
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- f1 |
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pipeline_tag: image-classification |
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library_name: pytorch |
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--- |
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# Cervical Cancer Classification CNN |
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A CNN model for classifying cervical colposcopy images into 4 severity classes for cervical cancer screening. |
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## Model Description |
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This model classifies cervical images into: |
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| Class | Label | Description | Clinical Action | |
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|-------|-------|-------------|-----------------| |
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| 0 | Normal | Healthy cervical tissue | Routine screening in 3-5 years | |
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| 1 | LSIL | Low-grade Squamous Intraepithelial Lesion | Monitor, repeat test in 6-12 months | |
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| 2 | HSIL | High-grade Squamous Intraepithelial Lesion | Colposcopy, biopsy, treatment required | |
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| 3 | Cancer | Invasive cervical cancer | Immediate oncology referral | |
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--- |
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## Model Architecture |
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### Architecture Diagram |
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``` |
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┌─────────────────────────────────────────────────────────────┐ |
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│ INPUT IMAGE │ |
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│ (3 × 224 × 298) │ |
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└─────────────────────────┬───────────────────────────────────┘ |
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│ |
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┌─────────────────────────▼───────────────────────────────────┐ |
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│ CONV BLOCK 1 │ |
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│ ├── Conv2d(3 → 32, kernel=3×3, padding=1) │ |
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│ ├── BatchNorm2d(32) │ |
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│ ├── ReLU │ |
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│ └── MaxPool2d(2×2) │ |
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│ Output: 32 × 112 × 149 │ |
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└─────────────────────────┬───────────────────────────────────┘ |
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│ |
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┌─────────────────────────▼───────────────────────────────────┐ |
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│ CONV BLOCK 2 │ |
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│ ├── Conv2d(32 → 64, kernel=3×3, padding=1) │ |
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│ ├── BatchNorm2d(64) │ |
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│ ├── ReLU │ |
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│ └── MaxPool2d(2×2) │ |
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│ Output: 64 × 56 × 74 │ |
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└─────────────────────────┬───────────────────────────────────┘ |
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│ |
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┌─────────────────────────▼───────────────────────────────────┐ |
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│ CONV BLOCK 3 │ |
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│ ├── Conv2d(64 → 128, kernel=3×3, padding=1) │ |
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│ ├── BatchNorm2d(128) │ |
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│ ├── ReLU │ |
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│ └── MaxPool2d(2×2) │ |
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│ Output: 128 × 28 × 37 │ |
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└─────────────────────────┬───────────────────────────────────┘ |
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│ |
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┌─────────────────────────▼───────────────────────────────────┐ |
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│ CONV BLOCK 4 │ |
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│ ├── Conv2d(128 → 256, kernel=3×3, padding=1) │ |
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│ ├── BatchNorm2d(256) │ |
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│ ├── ReLU │ |
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│ └── MaxPool2d(2×2) │ |
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│ Output: 256 × 14 × 18 │ |
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└─────────────────────────┬───────────────────────────────────┘ |
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│ |
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┌─────────────────────────▼───────────────────────────────────┐ |
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│ GLOBAL AVERAGE POOLING │ |
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│ └── AdaptiveAvgPool2d(1×1) │ |
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│ Output: 256 × 1 × 1 → Flatten → 256 │ |
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└─────────────────────────┬───────────────────────────────────┘ |
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│ |
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┌─────────────────────────▼───────────────────────────────────┐ |
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│ FC BLOCK 1 │ |
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│ ├── Linear(256 → 256) │ |
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│ ├── ReLU │ |
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│ └── Dropout(0.5) │ |
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└─────────────────────────┬───────────────────────────────────┘ |
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│ |
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┌─────────────────────────▼───────────────────────────────────┐ |
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│ FC BLOCK 2 │ |
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│ ├── Linear(256 → 128) │ |
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│ ├── ReLU │ |
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│ └── Dropout(0.5) │ |
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└─────────────────────────┬───────────────────────────────────┘ |
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│ |
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┌─────────────────────────▼───────────────────────────────────┐ |
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│ CLASSIFIER │ |
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│ └── Linear(128 → 4) │ |
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│ Output: 4 class logits │ |
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└─────────────────────────┬───────────────────────────────────┘ |
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│ |
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▼ |
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[Normal, LSIL, HSIL, Cancer] |
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``` |
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### Architecture Summary Table |
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| Layer | Type | Input Shape | Output Shape | Parameters | |
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|-------|------|-------------|--------------|------------| |
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| conv_layers.0 | Conv2d | (3, 224, 298) | (32, 224, 298) | 896 | |
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| conv_layers.1 | BatchNorm2d | (32, 224, 298) | (32, 224, 298) | 64 | |
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| conv_layers.2 | ReLU | - | - | 0 | |
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| conv_layers.3 | MaxPool2d | (32, 224, 298) | (32, 112, 149) | 0 | |
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| conv_layers.4 | Conv2d | (32, 112, 149) | (64, 112, 149) | 18,496 | |
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| conv_layers.5 | BatchNorm2d | (64, 112, 149) | (64, 112, 149) | 128 | |
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| conv_layers.6 | ReLU | - | - | 0 | |
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| conv_layers.7 | MaxPool2d | (64, 112, 149) | (64, 56, 74) | 0 | |
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| conv_layers.8 | Conv2d | (64, 56, 74) | (128, 56, 74) | 73,856 | |
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| conv_layers.9 | BatchNorm2d | (128, 56, 74) | (128, 56, 74) | 256 | |
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| conv_layers.10 | ReLU | - | - | 0 | |
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| conv_layers.11 | MaxPool2d | (128, 56, 74) | (128, 28, 37) | 0 | |
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| conv_layers.12 | Conv2d | (128, 28, 37) | (256, 28, 37) | 295,168 | |
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| conv_layers.13 | BatchNorm2d | (256, 28, 37) | (256, 28, 37) | 512 | |
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| conv_layers.14 | ReLU | - | - | 0 | |
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| conv_layers.15 | MaxPool2d | (256, 28, 37) | (256, 14, 18) | 0 | |
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| avgpool | AdaptiveAvgPool2d | (256, 14, 18) | (256, 1, 1) | 0 | |
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| fc_layers.0 | Linear | 256 | 256 | 65,792 | |
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| fc_layers.1 | ReLU | - | - | 0 | |
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| fc_layers.2 | Dropout | - | - | 0 | |
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| fc_layers.3 | Linear | 256 | 128 | 32,896 | |
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| fc_layers.4 | ReLU | - | - | 0 | |
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| fc_layers.5 | Dropout | - | - | 0 | |
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| classifier | Linear | 128 | 4 | 516 | |
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| **Total** | | | | **488,580** | |
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### PyTorch Model Code |
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```python |
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import torch |
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import torch.nn as nn |
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class CervicalCancerCNN(nn.Module): |
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def __init__(self): |
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super().__init__() |
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# Convolutional layers: [32, 64, 128, 256] |
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self.conv_layers = nn.Sequential( |
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# Block 1: 3 -> 32 |
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nn.Conv2d(3, 32, kernel_size=3, padding=1), |
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nn.BatchNorm2d(32), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(2, 2), |
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# Block 2: 32 -> 64 |
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nn.Conv2d(32, 64, kernel_size=3, padding=1), |
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nn.BatchNorm2d(64), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(2, 2), |
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# Block 3: 64 -> 128 |
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nn.Conv2d(64, 128, kernel_size=3, padding=1), |
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nn.BatchNorm2d(128), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(2, 2), |
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# Block 4: 128 -> 256 |
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nn.Conv2d(128, 256, kernel_size=3, padding=1), |
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nn.BatchNorm2d(256), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(2, 2), |
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) |
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self.avgpool = nn.AdaptiveAvgPool2d(1) |
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# Fully connected layers: [256, 128] -> 4 |
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self.fc_layers = nn.Sequential( |
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nn.Linear(256, 256), |
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nn.ReLU(inplace=True), |
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nn.Dropout(0.5), |
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nn.Linear(256, 128), |
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nn.ReLU(inplace=True), |
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nn.Dropout(0.5), |
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) |
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self.classifier = nn.Linear(128, 4) |
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def forward(self, x): |
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x = self.conv_layers(x) |
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x = self.avgpool(x) |
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x = x.view(x.size(0), -1) |
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x = self.fc_layers(x) |
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x = self.classifier(x) |
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return x |
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``` |
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--- |
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## Performance |
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### Overall Metrics |
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| Metric | Value | |
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|--------|-------| |
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| **Accuracy** | 59.52% | |
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| **Macro F1** | 59.85% | |
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| **Parameters** | 488,580 | |
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### Per-Class Metrics |
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| Class | Precision | Recall | F1 Score | Support | |
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|-------|-----------|--------|----------|---------| |
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| Normal | 0.595 | 0.595 | 0.595 | 84 | |
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| LSIL | 0.521 | 0.583 | 0.551 | 84 | |
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| HSIL | 0.446 | 0.440 | 0.443 | 84 | |
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| Cancer | 0.853 | 0.762 | 0.805 | 84 | |
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### Confusion Matrix |
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``` |
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Predicted → Normal LSIL HSIL Cancer |
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Actual ↓ |
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Normal 50 9 17 8 |
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LSIL 24 49 11 0 |
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HSIL 9 35 37 3 |
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Cancer 1 1 18 64 |
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``` |
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--- |
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## Usage |
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### Installation |
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```bash |
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pip install torch torchvision safetensors huggingface_hub |
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``` |
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### Loading the Model |
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```python |
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import torch |
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from safetensors.torch import load_file |
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from huggingface_hub import hf_hub_download |
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import json |
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# Download model files |
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model_file = hf_hub_download("toderian/cerviguard_lesion", "model.safetensors") |
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config_file = hf_hub_download("toderian/cerviguard_lesion", "config.json") |
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# Load config |
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with open(config_file) as f: |
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config = json.load(f) |
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# Define model (copy from above or download modeling_cervical.py) |
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model = CervicalCancerCNN() |
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# Load weights |
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state_dict = load_file(model_file) |
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model.load_state_dict(state_dict) |
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model.eval() |
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``` |
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### Inference |
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```python |
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from PIL import Image |
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import torchvision.transforms as T |
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# Preprocessing |
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transform = T.Compose([ |
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T.Resize((224, 298)), |
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T.ToTensor(), |
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T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
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]) |
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# Load and preprocess image |
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image = Image.open("cervical_image.jpg").convert("RGB") |
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input_tensor = transform(image).unsqueeze(0) |
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# Inference |
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with torch.no_grad(): |
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output = model(input_tensor) |
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probabilities = torch.softmax(output, dim=1) |
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prediction = output.argmax(dim=1).item() |
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classes = ["Normal", "LSIL", "HSIL", "Cancer"] |
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print(f"Prediction: {classes[prediction]}") |
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print(f"Confidence: {probabilities[0][prediction]:.2%}") |
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``` |
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--- |
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## Training Details |
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| Parameter | Value | |
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|-----------|-------| |
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| Learning Rate | 1e-4 | |
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| Batch Size | 32 | |
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| Optimizer | Adam | |
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| Loss | CrossEntropyLoss | |
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| Dropout | 0.5 | |
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| Epochs | 34 (early stopping at 24) | |
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### Dataset |
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| Split | Samples | Distribution | |
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|-------|---------|--------------| |
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| Train | 3,003 | Imbalanced [1540, 469, 854, 140] | |
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| Test | 336 | Balanced [84, 84, 84, 84] | |
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--- |
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## Limitations |
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- Trained on limited dataset (~3k samples) |
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- HSIL class has lowest performance (F1=0.443) |
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- Should not be used as sole diagnostic tool |
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- Intended for research and screening assistance only |
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## Medical Disclaimer |
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⚠️ **This model is for research purposes only.** It should not be used as a substitute for professional medical diagnosis. Always consult qualified healthcare professionals for cervical cancer screening and diagnosis. |
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--- |
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## Files in This Repository |
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| File | Description | |
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|------|-------------| |
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| `model.safetensors` | Model weights (safetensors format) | |
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| `pytorch_model.bin` | Model weights (legacy PyTorch format) | |
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| `config.json` | Model configuration | |
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| `preprocessor_config.json` | Image preprocessing settings | |
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| `modeling_cervical.py` | Model class definition | |
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| `example_inference.py` | Example inference script | |
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--- |
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## Citation |
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```bibtex |
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@misc{cervical-cancer-cnn-2025, |
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author = {Toderian}, |
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title = {Cervical Cancer Classification CNN}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/toderian/cerviguard_lesion} |
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} |
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``` |
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