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<p>The identification of cancerous tissues remains challenging due to the complexity of experimental methods and low identification accuracy rates. Therefore, this paper proposes a rapid identification method. We introduce a new theoretical transmission method for modeling laser beams transport in cancerous and normal biological tissues. Using this method, laser speckle patterns carrying tissue information are obtained at the light transmission receiving plane. Then, we propose a three-task residual neural network, T-ResNet-18, for identifying speckle images. We simulate the human normal and cancerous prostate tissues, rat normal and cancerous tissues, and normal and abnormal cell suspension as training samples. The results show the identification accuracy exceeding 99%. Additionally, we discuss the impact of varying dataset sizes, training epochs, and tissue thickness on identification accuracy and compare the performance of T-ResNet-18 with ResNet-18, VGG16 and AlexNet, showing that T-ResNet-18 significantly outperforms classic neural networks.</p>

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Identification of cancerous tissues based on residual neural network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月17日

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  • Ying Liu,
  • Xiaoyun Liu,
  • Siyu Gao,
  • Tengfei Chai,
  • Zihao Zhao,
  • Hongwei Wang,
  • Yumeihui Jin,
  • Yueqiu Jiang

摘要

The identification of cancerous tissues remains challenging due to the complexity of experimental methods and low identification accuracy rates. Therefore, this paper proposes a rapid identification method. We introduce a new theoretical transmission method for modeling laser beams transport in cancerous and normal biological tissues. Using this method, laser speckle patterns carrying tissue information are obtained at the light transmission receiving plane. Then, we propose a three-task residual neural network, T-ResNet-18, for identifying speckle images. We simulate the human normal and cancerous prostate tissues, rat normal and cancerous tissues, and normal and abnormal cell suspension as training samples. The results show the identification accuracy exceeding 99%. Additionally, we discuss the impact of varying dataset sizes, training epochs, and tissue thickness on identification accuracy and compare the performance of T-ResNet-18 with ResNet-18, VGG16 and AlexNet, showing that T-ResNet-18 significantly outperforms classic neural networks.