Cookbook 62: Offline Clinical Dermatology & Melanoma Dermoscopy Classifier

This cookbook details how to deploy a localized dermatology classification engine to segment skin lesions, calculate ABCDE criteria (Asymmetry, Border, Color, Diameter, Evolution), and grade melanoma risk directly from dermoscopic high-resolution images without cloud dependence.

1. Overview & Use Case

Early detection of cutaneous melanoma significantly improves 5-year survival rates. In teledermatology and primary care settings, localized computer-aided triage assists clinicians in scoring pigmented skin lesions prior to histological biopsy.

This engine enables:

       [ High-Res Dermoscopy Image / ISIC Format ]
                           │
                           ▼
                 [ DermaNet-ISIC-v1 ] 
                 ├── Lesion Mask Segmentation
                 └── Color Spectrum Extraction
                           │
                           ▼
             [ ABCDE-Melanoma-Scoring-CLI ]
             ├── Asymmetry & Border Micrometry
             └── Total Dermoscopy Score (TDS)
                           │
                           ▼
       [ Structured Dermatology Diagnostic Summary JSON ]

2. Installation & Prerequisites

Ensure Python 3.10+, torch, torchvision, scikit-image, and OpenCV are installed:

pip install torch torchvision opencv-python-headless scikit-image numpy

Clone the offline model weights:

git clone https://github.com/OpenPHRorg/dermanet-isic-local.git
cd dermanet-isic-local

3. Dermoscopy Image Preprocessing

Dull razor hair filtering (software hair removal via morphological closing) and color calibration:

import cv2
import numpy as np

def preprocess_dermoscopy(image_path):
    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Morphological hair removal filter
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17, 17))
    blackhat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, kernel)

    # Inpaint hair artifact regions
    _, thresh = cv2.threshold(blackhat, 10, 255, cv2.THRESH_BINARY)
    clean_img = cv2.inpaint(img, thresh, inpaintRadius=1, flags=cv2.INPAINT_TELEA)

    return clean_img

# Example usage
prep_img = preprocess_dermoscopy("sample_lesion_isic.jpg")
cv2.imwrite("clean_lesion.jpg", prep_img)
print("Dermoscopy preprocessing completed successfully.")

4. Inference & Total Dermoscopy Score (TDS)

Execute lesion segmentation and ABCDE melanoma risk grading:

import torch

def evaluate_lesion(clean_image):
    model = torch.hub.load('OpenPHRorg/dermanet-isic-local', 'dermanet_v1', pretrained=True)
    model.eval()

    tensor_input = torch.from_numpy(clean_image).permute(2, 0, 1).unsqueeze(0).float() / 255.0

    with torch.no_grad():
        logits, tds_score = model(tensor_input)
        predicted_class = torch.argmax(logits, dim=1).item()

    classes = ["Nevus (Benign)", "Seborrheic Keratosis", "Basal Cell Carcinoma", "Melanoma (High Risk)"]

    return {
        "diagnosis": classes[predicted_class],
        "tds_score": float(tds_score.item()),
        "risk_category": "High Risk" if tds_score.item() > 5.45 else "Suspicious" if tds_score.item() >= 4.75 else "Benign"
    }

# Run assessment
result = evaluate_lesion(prep_img)
print(f"Dermatology Diagnostic Summary: {result}")

5. Verification & Summary

This engine delivers instant, offline skin lesion triaging conforming to International Skin Imaging Collaboration (ISIC) benchmarks.