Scholarlink

Skin cancer ranks as one of the most widespread cancers globally, making early and accurate diagnosis essential for better patient care. This study proposes an innovative Advanced Convolutional Neural Network (ACNN) model to enhance the precision of skin cancer detection. The proposed ACNN model integrates traditional CNN architecture with optimization techniques such as attention mechanisms and transfer learning to improve classification accuracy. By leveraging a curated dataset of dermoscopic images, the ACNN is trained to classify various types of skin lesions, including malignant melanoma and benign nevi, with high precision.

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

Enhanced Skin Cancer Detection Using Convolution Neural Network

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Nissi Biju Varghese,
  • D. Narmadha,
  • G. Naveen Sundar,
  • K. Martin Sagayam,
  • Suman Majumder,
  • Sangram Ray,
  • Pronaya Bhattacharya

摘要

Skin cancer ranks as one of the most widespread cancers globally, making early and accurate diagnosis essential for better patient care. This study proposes an innovative Advanced Convolutional Neural Network (ACNN) model to enhance the precision of skin cancer detection. The proposed ACNN model integrates traditional CNN architecture with optimization techniques such as attention mechanisms and transfer learning to improve classification accuracy. By leveraging a curated dataset of dermoscopic images, the ACNN is trained to classify various types of skin lesions, including malignant melanoma and benign nevi, with high precision.