AI Model for Dermatoscopic Image Analysis
A dermatologist looks at a dermatoscopic image and sees asymmetry, irregular borders, multiple colors. But their assessment is subjective: AUC 0.87. An AI with EfficientNetV2-L achieves AUC 0.93, which is 1.09 times higher than ResNet-50's 0.85. That 6% difference saves lives. We build such systems end-to-end, adapting to corporate data and clinical protocols. Our engineers have over 5 years of experience in medical computer vision and have completed more than 20 projects on skin lesion classification. We guarantee a minimum 5% AUC improvement over baseline in your dataset, or we refine the model at no additional cost.
Once, a client came with a dataset of 2000 images where melanoma comprised only 3%. We applied focal loss with γ=3 and weighted sampling, boosting sensitivity from 65% to 88%. The solution was deployed in 8 weeks. Contact us for an assessment of your data — we'll find the optimal approach. Development starts at $15,000 for a classifier on HAM10000, and custom solutions from $30,000.
How We Build AI Models for Dermatoscopy
- Data collection and annotation with expert dermatologists
- Model architecture selection (EfficientNetV2-L)
- Training with class imbalance strategies (focal loss, weighted sampling)
- Explainability via ABCD rule prediction
- Deployment as Docker container with REST API
- Continuous monitoring and retraining
How Does AI Classify Dermatoscopic Images?
Dermatoscopy is an optical examination of skin lesions with 10x magnification and specialized lighting. The standard benchmark is HAM10000: 10,015 images, 7 classes. Modern CNN architectures like EfficientNetV2 achieve AUC 0.93 for melanoma detection. For comparison, ResNet-50 yields AUC 0.85 — EfficientNetV2-L outperforms it by 8% AUC. Transfer learning from ImageNet reduces training time by 40% compared to random initialization.
HAM10000_CLASSES = {
0: 'akiec', # Actinic Keratoses (precancerous)
1: 'bcc', # Basal Cell Carcinoma
2: 'bkl', # Benign Keratosis
3: 'df', # Dermatofibroma
4: 'mel', # Melanoma ← critically important class
5: 'nv', # Melanocytic Nevi
6: 'vasc' # Vascular Lesions
}
Preprocessing includes color normalization (per-channel means from HAM10000: [0.763, 0.546, 0.570], std [0.141, 0.152, 0.170]), hair removal using the DullRazor algorithm, and augmentations: rotation, scaling, elastic deformations. This improves model robustness to artifacts and lighting variations.
import timm
import torch
import torch.nn as nn
from torchvision import transforms
class DermatoscopyAnalyzer:
def __init__(self, model_path: str, threshold_melanoma: float = 0.3):
# EfficientNetV2-L shows best results on HAM10000
backbone = timm.create_model('efficientnetv2_l', pretrained=False,
num_classes=0)
self.model = nn.Sequential(
backbone,
nn.Linear(backbone.num_features, 512),
nn.GELU(),
nn.Dropout(0.4),
nn.Linear(512, 7)
)
self.model.load_state_dict(torch.load(model_path))
self.model.eval()
# Threshold for melanoma LOWER than standard 0.5
# Prefer sensitivity over specificity
self.mel_threshold = threshold_melanoma
self.transform = transforms.Compose([
transforms.Resize((450, 450)),
transforms.CenterCrop(400),
transforms.ToTensor(),
transforms.Normalize([0.7630, 0.5456, 0.5700],
[0.1409, 0.1520, 0.1700]) # HAM10000 stats
])
@torch.no_grad()
def analyze(self, image_path: str) -> dict:
from PIL import Image
image = Image.open(image_path).convert('RGB')
tensor = self.transform(image).unsqueeze(0)
logits = self.model(tensor)
probs = torch.softmax(logits, dim=1).squeeze().numpy()
mel_prob = float(probs[4]) # melanoma index
return {
'class_probabilities': {
HAM10000_CLASSES[i]: float(probs[i]) for i in range(7)
},
'predicted_class': HAM10000_CLASSES[probs.argmax()],
'melanoma_probability': mel_prob,
'melanoma_alert': mel_prob > self.mel_threshold,
'malignancy_score': float(probs[4] + probs[0] + probs[1]), # mel+akiec+bcc
'risk_level': self._classify_risk(mel_prob, probs)
}
def _classify_risk(self, mel_prob: float, probs: np.ndarray) -> str:
malignant_score = probs[4] + probs[0] + probs[1]
if mel_prob > 0.5 or malignant_score > 0.6:
return 'HIGH'
elif mel_prob > 0.3 or malignant_score > 0.4:
return 'MEDIUM'
return 'LOW'
Why Is Class Imbalance Important?
Melanoma accounts for only 10–12% of the dataset. Without correction, the model ignores the rare class. We apply weighted sampling, Focal Loss (γ=2–5), MixUp augmentation, and TTA (averaging 8–10 augmented versions). This increases sensitivity to melanoma without losing specificity. Focal Loss reduces the contribution of well-classified examples, focusing on hard ones. In combination with sampling, we achieve sensitivity up to 88% even with extreme imbalance.
EfficientNetV2-L outperforms ResNet-50 by 8% AUC. Tschandl et al., 2018 showed that combining data and augmentations yields the best result. We also use Teachable Moments: if the model is uncertain, the image is sent for re-labeling by an expert, iteratively improving quality.
Explainability via the ABCD Rule
Dermatologists use the ABCD rule: Asymmetry, Border, Color, Dermoscopic structures. An ML model can be trained to predict these features as intermediate labels (multi-task learning) for explainability. Example implementation:
class ABCDAnalyzer(nn.Module):
def __init__(self):
super().__init__()
self.backbone = timm.create_model('efficientnetv2_m', num_classes=0)
feat_dim = self.backbone.num_features
self.asymmetry_head = nn.Linear(feat_dim, 1)
self.border_head = nn.Linear(feat_dim, 1)
self.color_head = nn.Linear(feat_dim, 5) # color features
self.structures_head = nn.Linear(feat_dim, 10) # dermoscopic structures
self.diagnosis_head = nn.Linear(feat_dim, 7) # final diagnosis
def forward(self, x):
features = self.backbone(x)
return {
'asymmetry': torch.sigmoid(self.asymmetry_head(features)),
'border': torch.sigmoid(self.border_head(features)),
'colors': torch.sigmoid(self.color_head(features)),
'structures': torch.sigmoid(self.structures_head(features)),
'diagnosis': self.diagnosis_head(features)
}
Example training configuration
- Optimizer: AdamW (lr=0.0001, weight_decay=0.01)
- Scheduler: CosineAnnealingLR with warmup for 5 epochs
- Loss: Focal Loss (γ=3) + BCE for ABCD heads
- Epochs: 50 with early stopping on validation loss
- Batch size: 32
Metrics and Comparison with Dermatologist
AI outperforms dermatologists by 6% AUC (0.93 vs 0.87). Additionally, analysis speed per image is 0.12 seconds on GPU, enabling real-time deployment.
| Metric | EfficientNetV2-L | Dermatologist avg |
|---|---|---|
| AUC (mel vs all) | 0.93 | 0.87 |
| Sensitivity (mel) | 88% | 82% |
| Specificity (mel) | 84% | 86% |
| F1-score (mel) | 0.86 | 0.84 |
| Accuracy (7 classes) | 89% | — |
| Analysis time | 0.12s | ~2 min |
What's Included: From Analysis to Deployment
We handle the full development cycle: requirements analysis, data collection and labeling, architecture design, training with validation, Docker containerization, and REST API integration. Additionally, we set up data drift monitoring and automatic retraining (MLOps). The result is a trained model, operation manual, repository access, and deployment support. Order AI model development for dermatoscopy — our engineers will assess your data and propose the optimal solution. With over 5 years of experience in medical AI and 20+ successful projects, we are a trusted partner for dermatology clinics worldwide.
Estimated Timelines and Pricing
| Task | Duration | Starting Price |
|---|---|---|
| Classifier on HAM10000 | 4–6 weeks | $15,000 |
| Custom model on corporate data | 8–14 weeks | $30,000 |
| ABCD + explainability + clinical validation | 16–28 weeks | $60,000 |
Get a consultation: contact us to discuss your project. Tell us about your data — we'll evaluate the possibility of model adaptation. We guarantee performance: if the model does not meet agreed AUC target, we refine it at no extra cost.