Scholarlink

<p>The diagnosis of skin diseases has been a focus of great interest because of the increased rates of skin disorders and the necessity of prompt and convenient diagnosis of dermatological disorders. In this paper, we introduce a machine-learning model of the multi-class classification of nine common skin diseases with the help of the customized Convolutional Neural Network (CNN). Eight hundred and seventy-eight dermoscopic images were acquired at Kaggle, processed, and augmented followed by classification using the proposed CNN architecture. The images were downsized to 200 × 200 pixels, and rescaled, rotated, sheared, zoomed and horizontally flipped. The model attained a training accuracy of 92.78% and the validation accuracy of 81%. Confusion matrix, precision, recall and F1-score were used in measuring performance. These findings suggest that CNN-based models may be useful in automated scripts of dermatological screening and can be used as the foundation of deployable diagnostic tools.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

GlowSkinFit: machine learning and CNN-based approaches for accurate skin disease detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月21日

查看全文
  • Anushree Dahiya,
  • Sambhav Chordia,
  • Satyanshu Yadav,
  • Shardul Kacheria,
  • Sudhanshu Suhas Gonge,
  • Deepak Parashar,
  • Nilesh Bahadure

摘要

The diagnosis of skin diseases has been a focus of great interest because of the increased rates of skin disorders and the necessity of prompt and convenient diagnosis of dermatological disorders. In this paper, we introduce a machine-learning model of the multi-class classification of nine common skin diseases with the help of the customized Convolutional Neural Network (CNN). Eight hundred and seventy-eight dermoscopic images were acquired at Kaggle, processed, and augmented followed by classification using the proposed CNN architecture. The images were downsized to 200 × 200 pixels, and rescaled, rotated, sheared, zoomed and horizontally flipped. The model attained a training accuracy of 92.78% and the validation accuracy of 81%. Confusion matrix, precision, recall and F1-score were used in measuring performance. These findings suggest that CNN-based models may be useful in automated scripts of dermatological screening and can be used as the foundation of deployable diagnostic tools.