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Early prediction of lung cancer is crucial for reducing the death rate. Artificial intelligence, particularly deep learning, is employed to analyze CT scan images for more accurate automated prediction of types of lung cancer. This process of prediction is called classification. Lung cancer classification can be done with pre-networks such as VGG16 and ResNet50.But the main drawback of these techniques is that cancer cannot be detected on Histopathology images (i.e. image of tissues). As VGG16 and ResNet50 are designed for more general usage, they are not suitable for analyzing Histopathology image. This study involves the development of a customized neural network model which can solve the problem of analyzing Histopathology images. This CNN model can help us detect lung cancer at a very early state in lung tissues. Detecting lung cancer at a very early stage can help doctors to cure the patient and save the life of a patient.

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Developing a CNN Model (Rebbica) for Effective Lung Cancer Classification on Histopathology Images

  • 会议论文

  • 发表日期：

    2026年

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  • N. N. S. S. S. Adithya,
  • P. Vanishree Sah,
  • B. Jyothirmai,
  • Nidhi Mishra,
  • D. Indira

摘要

Early prediction of lung cancer is crucial for reducing the death rate. Artificial intelligence, particularly deep learning, is employed to analyze CT scan images for more accurate automated prediction of types of lung cancer. This process of prediction is called classification. Lung cancer classification can be done with pre-networks such as VGG16 and ResNet50.But the main drawback of these techniques is that cancer cannot be detected on Histopathology images (i.e. image of tissues). As VGG16 and ResNet50 are designed for more general usage, they are not suitable for analyzing Histopathology image. This study involves the development of a customized neural network model which can solve the problem of analyzing Histopathology images. This CNN model can help us detect lung cancer at a very early state in lung tissues. Detecting lung cancer at a very early stage can help doctors to cure the patient and save the life of a patient.