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The significance of a deep learning-based approach for the classification of lung cancer using chest X-ray images. The proposed model uses Convolutional Neural Networks (CNNs) and transfer learning techniques to accurately classify lung X-ray images into four categories: Large Cell Carcinoma, Normal and Adenocarcinoma, and Squamous Cell Carcinoma. The system incorporates the VGG16 architecture pre-trained on ImageNet for feature extraction, followed by fine-tuning and training on a dataset of labeled chest X-ray images. Auditing of the model consists of qualitative measurements, including accuracy and loss of the model and the confusion matrices that shed light on the model. With the help of the developed system, there is a potential to help radiologists to discover and diagnose the initial stages of lung cancer thus enhancing patient care and healthcare delivery.

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Deep Learning-Based Lung Cancer Classification with Chest X-Ray

  • 会议论文

  • 发表日期：

    2025年

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  • Tella Anil Raju,
  • B. Rajeswari,
  • Meghana Palagani,
  • Allam Papa Rao

摘要

The significance of a deep learning-based approach for the classification of lung cancer using chest X-ray images. The proposed model uses Convolutional Neural Networks (CNNs) and transfer learning techniques to accurately classify lung X-ray images into four categories: Large Cell Carcinoma, Normal and Adenocarcinoma, and Squamous Cell Carcinoma. The system incorporates the VGG16 architecture pre-trained on ImageNet for feature extraction, followed by fine-tuning and training on a dataset of labeled chest X-ray images. Auditing of the model consists of qualitative measurements, including accuracy and loss of the model and the confusion matrices that shed light on the model. With the help of the developed system, there is a potential to help radiologists to discover and diagnose the initial stages of lung cancer thus enhancing patient care and healthcare delivery.