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<p>In this study, a one-dimensional convolutional neural network (1D-CNN) was developed for classifying serum Raman spectra into three oral cancer-related classes: normal, precancerous, and cancerous. To address class imbalance, class-weighted learning was employed during training, while augmentation was evaluated separately as a comparative strategy. The final selected non-augmented model achieved an overall testing accuracy of 83%, with a macro-precision of 85%, macro-recall of 84%, and macro-F1 score of 83%, indicating balanced three-class performance. To further assess real-world applicability, an independent test dataset collected later from the same hospital was used. This cohort followed the same acquisition and preprocessing protocol but consisted of entirely new patients. The model maintained consistent classification performance on this independent dataset, supporting its generalizability within the same clinical environment. These findings highlight the potential of combining Raman spectroscopy with deep learning as a non-invasive framework for oral cancer-related biosample classification.</p>

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Training and validation of a 1D CNN model for accurate three-class classification of oral cancer using serum Raman spectroscopy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月14日

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  • Mukta Sharma,
  • Shiang-Fu Huang

摘要

In this study, a one-dimensional convolutional neural network (1D-CNN) was developed for classifying serum Raman spectra into three oral cancer-related classes: normal, precancerous, and cancerous. To address class imbalance, class-weighted learning was employed during training, while augmentation was evaluated separately as a comparative strategy. The final selected non-augmented model achieved an overall testing accuracy of 83%, with a macro-precision of 85%, macro-recall of 84%, and macro-F1 score of 83%, indicating balanced three-class performance. To further assess real-world applicability, an independent test dataset collected later from the same hospital was used. This cohort followed the same acquisition and preprocessing protocol but consisted of entirely new patients. The model maintained consistent classification performance on this independent dataset, supporting its generalizability within the same clinical environment. These findings highlight the potential of combining Raman spectroscopy with deep learning as a non-invasive framework for oral cancer-related biosample classification.