Scholarlink

Breast cancer is not only the lifeblood of mortality among women but also makes it very critical to diagnose patients at an early stage with high accuracy, and this research is depending upon introducing “convolutional neural networks” for breast cancer detection. A “deep learning”-based study for mammography and thermography images to enhance the accuracy of diagnostics The CNN-based model was trained to analyze the imaging data, defining the features specific to breast cancer. A very big database is used to train and test the neural net and it arrives at an accuracy of 97.08%. This clearly shows that convolutional neural networks can offer a noninvasive and efficient route to diagnosis, rather than usual ones. The technique also emphasizes the need for early detection, which is very important for improving patient outcomes. The future study shall include some investigation about other modalities or real-time diagnosis systems. It also shows how deep learning causes a paradigm shift in breast cancer diagnosis and treatment.

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

Convolutional Neural Networks for Accurate Breast Cancer Diagnosis: A Deep Learning Approach

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Raj Majumdar,
  • Pritha Ghosh,
  • Soham Modak,
  • Trisha Mallick,
  • Aritra Biswas,
  • Biswarup Yogi

摘要

Breast cancer is not only the lifeblood of mortality among women but also makes it very critical to diagnose patients at an early stage with high accuracy, and this research is depending upon introducing “convolutional neural networks” for breast cancer detection. A “deep learning”-based study for mammography and thermography images to enhance the accuracy of diagnostics The CNN-based model was trained to analyze the imaging data, defining the features specific to breast cancer. A very big database is used to train and test the neural net and it arrives at an accuracy of 97.08%. This clearly shows that convolutional neural networks can offer a noninvasive and efficient route to diagnosis, rather than usual ones. The technique also emphasizes the need for early detection, which is very important for improving patient outcomes. The future study shall include some investigation about other modalities or real-time diagnosis systems. It also shows how deep learning causes a paradigm shift in breast cancer diagnosis and treatment.