{
  "id": 401667,
  "title": "A hybrid deep feature optimized ensemble framework for multiclass skin disease classification",
  "url": "https://urgent.news/2026/08/09/a-hybrid-deep-feature-optimized-ensemble-framework-for-multiclass",
  "topic": "science",
  "section": "Science",
  "published": "2026-08-09T00:00:00.000Z",
  "source": {
    "name": "Scientific Reports",
    "slug": "scientific-reports",
    "url": "https://www.nature.com/articles/s41598-026-65115-1"
  },
  "original_language": "en",
  "account": "Skin conditions are among the most common dermatological disorders globally, and their accurate early diagnosis is crucial to minimizing diagnostic delays and associated mortality. This study introduces a new hybrid framework named ResBR-XGBR to classify skin lesions using HAM10000 dataset. The proposed method combines deep feature extraction with ResNet50 and EfficientNet-B0 models, followed by feature optimization using Boruta, Chi-square, and Recursive Feature Elimination (RFE) techniques to select the most discriminative features. Hyperparameter tuning was performed on 80:20 training-testing split datasets, employing Grid Search Cross validation for optimal learning. The ResBR-XGBR ensemble framework outperformed individual classifiers, attaining a remarkable 95.2% test classification accuracy.",
  "summary": "Scientific Reports, Published online: 09 August 2026; doi:10.1038/s41598-026-65115-1 A hybrid deep feature optimized ensemble framework for multiclass skin disease classification",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}