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<p>This study introduces a deep learning methodology for the automated identification of tissue regions indicative of Invasive Ductal Carcinoma (IDC) within Whole Slide Images (WSI) associated with breast cancer. Deep learning demonstrates notable efficacy in such applications, particularly when an ample number of samples are available for training purposes. The proposed framework extends across various convolutional neural networks (CNNs). The method underwent evaluation using a WSI dataset encompassing specimens from 162 patients diagnosed with IDC. The experimental outcomes indicate commendable accuracies of 89%, 88%, 84%, and 82%, for CNN, EfficientNet, EfficientNetV2, and VGG16, respectively.</p>

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Invasive ductal carcinoma (IDC) detection in breast histopathology images using enhanced transfer learning of convolutional neural networks

  • 期刊论文

  • 发表日期：

    2025年4月29日

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  • Hafsa Binte Mahbub,
  • Akeem Olowolayemo,
  • Swaleh Maulid Omari,
  • Imran Ademola Adeleke

摘要

This study introduces a deep learning methodology for the automated identification of tissue regions indicative of Invasive Ductal Carcinoma (IDC) within Whole Slide Images (WSI) associated with breast cancer. Deep learning demonstrates notable efficacy in such applications, particularly when an ample number of samples are available for training purposes. The proposed framework extends across various convolutional neural networks (CNNs). The method underwent evaluation using a WSI dataset encompassing specimens from 162 patients diagnosed with IDC. The experimental outcomes indicate commendable accuracies of 89%, 88%, 84%, and 82%, for CNN, EfficientNet, EfficientNetV2, and VGG16, respectively.