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AventIQ-AI/
mobilenetv2_skin_cancer_detection

Name: mobilenetv2_skin_cancer_detection
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Model Card: Fine-Tuned MobileNetV2 for Skin Lesion Classification

Model Details

Model Name: Fine-Tuned MobileNetV2 for Skin Lesion Classification Base Model: google/mobilenet_v2_1.0_224 (pretrained on ImageNet) Dataset: marmal88/skin_cancer Quantization: Available as an optional FP16 version for optimized inference Training Device: CUDA (GPU, 12 GB)

Dataset Information

Dataset Structure

DatasetDict({
    train: Dataset({
        features: ['image', 'image_id', 'lesion_id', 'dx', 'dx_type', 'age', 'sex', 'localization'],
        num_rows: 9577
    })
    validation: Dataset({
        features: ['image', 'image_id', 'lesion_id', 'dx', 'dx_type', 'age', 'sex', 'localization'],
        num_rows: 2492
    })
    test: Dataset({
        features: ['image', 'image_id', 'lesion_id', 'dx', 'dx_type', 'age', 'sex', 'localization'],
        num_rows: 1285
    })
})
Available Splits
Train: 9,577 examples
Validation: 2,492 examples
Test: 1,285 examples

Feature Representation

image: RGB image (originally 600x450, resized to 224x224 during preprocessing)
image_id: Unique identifier (e.g., ISIC_0024329)
lesion_id: Lesion identifier (e.g., HAM_0002954)
dx: Diagnosis label (e.g., melanoma, actinic_keratoses)
dx_type: Diagnosis method (e.g., histo for histopathology)
age: Patient age (float, e.g., 75.0)
sex: Patient gender (e.g., female)
localization: Body location (e.g., lower extremity)

Note: Only image and dx (converted to integer labels) were used for training; other features were dropped during preprocessing.

Training Details

Number of Classes: 7
Class Names: actinic_keratoses, basal_cell_carcinoma, benign_keratosis-like_lesions, dermatofibroma, melanocytic_Nevi, melanoma, vascular_lesions
Training Process: Fine-tuned for 5 epochs (initially planned for 10, reduced to 5)
Learning Rate: 0.001 (Adam optimizer, fine-tuning all layers)
Batch Size: 32 (suitable for a 12 GB GPU)
Preprocessing: Images resized to 224x224, normalized with mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] (ImageNet stats)
Performance Metrics
Epochs: 5
Training Loss: [To be filled after training output]
Validation Loss: [To be filled after training output]
Accuracy: [To be filled after training output]
F1 Score: Not computed (can be added with additional evaluation)

Note: Performance metrics depend on your training output. Please provide the training log (loss/accuracy per epoch) to complete this section.

Inference Example

python

import torch
from torchvision import transforms
from PIL import Image
import torch.nn.functional as F
import json

# Preprocessing (matches training)
preprocess = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Load model and labels
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = "skin_cancer_model_fp16/mobilenetv2_skin_cancer_fp16.pt"
model = torch.load(model_path, map_location=device)
model = model.to(device)
model.eval()

with open("skin_cancer_model_fp16/labels.json", 'r') as f:
    label_mapping = json.load(f)
class_names = list(label_mapping.keys())

# Inference function
def predict_image(image_path, model, preprocess, device, class_names):
    image = Image.open(image_path).convert('RGB')
    image_tensor = preprocess(image).unsqueeze(0).half()  # FP16
    image_tensor = image_tensor.to(device)
    
    with torch.no_grad():
        outputs = model(image_tensor)
        probabilities = F.softmax(outputs, dim=1)
        confidence, predicted = torch.max(probabilities, 1)
        predicted_class = class_names[predicted.item()]
        confidence_score = confidence.item() * 100
    
    return predicted_class, confidence_score

# Example usage
if __name__ == "__main__":
    image_path = "C:/path/to/your/image.jpg"  # Replace with your image path
    predicted_class, confidence = predict_image(image_path, model, preprocess, device, class_names)
    print(f"Predicted Class: {predicted_class}")
    print(f"Confidence: {confidence:.2f}%")

Quantization & Optimization

Quantization: Optional FP16 version created using PyTorch’s .half() for faster inference and reduced memory footprint (~50% size reduction). Optimized: Suitable for deployment on GPU-enabled devices (e.g., CUDA with 12 GB VRAM).

Usage

Input: RGB images (any size, resized to 224x224 during preprocessing)
Output: Predicted skin lesion class (one of 7) with confidence probability

Limitations

Generalization: Trained on the marmal88/skin_cancer dataset, which may not fully represent all real-world skin lesions (e.g., varying lighting, angles, or skin types).
Dataset Bias: Performance may vary depending on dataset diversity (e.g., age, sex, localization not used in training).
Accuracy: Limited to 5 epochs; further training or larger datasets might improve results.

Future Improvements

Data Augmentation: Add rotation, flipping, or color jittering to enhance robustness.
Larger Dataset: Incorporate additional skin cancer datasets (e.g., ISIC Archive) for better coverage.
Model Tuning: Experiment with freezing feature layers or adjusting learning rate for better convergence.

AventIQ-AI/mobilenetv2_skin_cancer_detection