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This study proposes a hybrid CNN-ANN architecture for lung cancer image classification on the LC105K dataset. Enhanced feature extraction and representation techniques improve classification accuracy. The model leverages CNN and ANN strengths, demonstrating superior performance compared to existing methods. Results show significant accuracy, precision, and recall improvements, offering a promising solution for computer-aided diagnosis.

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Enhanced Feature Extraction and Representation in Hybrid CNN-ANN Architecture for Medical Image Classification Using LC105K Dataset

  • 会议论文

  • 发表日期：

    2025年

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  • Suresh Rasappan,
  • Ahamed Nishath,
  • Francis Saviour Devaraj

摘要

This study proposes a hybrid CNN-ANN architecture for lung cancer image classification on the LC105K dataset. Enhanced feature extraction and representation techniques improve classification accuracy. The model leverages CNN and ANN strengths, demonstrating superior performance compared to existing methods. Results show significant accuracy, precision, and recall improvements, offering a promising solution for computer-aided diagnosis.