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<p>Early detection of colorectal cancer is vital for enhancing cure rates and alleviating treatment burdens. Nevertheless, the high demand for screenings coupled with a limited number of endoscopists underscores the necessity for advanced deep learning techniques to improve screening efficiency and accuracy. This study presents an innovative convolutional neural network (CNN) model, trained on 8260 images from screenings conducted at four medical institutions. The model incorporates parallel global and local feature extraction branches and a distinctive classification head, facilitating both cancer classification and the creation of heatmaps that outline cancerous lesion regions. Performance evaluations of the CNN model, measured against five leading models using accuracy, precision, recall, and F1 score, revealed its superior efficacy across these metrics. Furthermore, the heatmaps proved effective in aiding the automatic identification of lesion locations. In summary, this CNN model represents a promising advancement in early colorectal cancer screening, delivering precise, swift diagnostic results and robust interpretability through its automatic lesion highlighting capabilities.</p>

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Utilizing a Novel Convolutional Neural Network for Diagnosis and Lesion Delineation in Colorectal Cancer Screening

  • 期刊论文

  • 发表日期：

    2025年1月17日

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  • Renbo Li,
  • Ruofan Cao,
  • Qi Zhao,
  • Zijian Zhao

摘要

Early detection of colorectal cancer is vital for enhancing cure rates and alleviating treatment burdens. Nevertheless, the high demand for screenings coupled with a limited number of endoscopists underscores the necessity for advanced deep learning techniques to improve screening efficiency and accuracy. This study presents an innovative convolutional neural network (CNN) model, trained on 8260 images from screenings conducted at four medical institutions. The model incorporates parallel global and local feature extraction branches and a distinctive classification head, facilitating both cancer classification and the creation of heatmaps that outline cancerous lesion regions. Performance evaluations of the CNN model, measured against five leading models using accuracy, precision, recall, and F1 score, revealed its superior efficacy across these metrics. Furthermore, the heatmaps proved effective in aiding the automatic identification of lesion locations. In summary, this CNN model represents a promising advancement in early colorectal cancer screening, delivering precise, swift diagnostic results and robust interpretability through its automatic lesion highlighting capabilities.