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Intraoperative Assessment of Resection Margins in Oral Cavity Cancer: This is the Way
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Chih-Hung Chan, Tze-Ta Huang, Chih-Yang Chen

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    This study introduces a novel deep convolutional neural network (DCNN) for automatic oral cancer detection and region of interest (ROI) marking. The texture-map-based branch-collaborative network achieves high sensitivity and specificity in identifying cancerous areas.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Accurate detection and marking of cancerous regions are crucial for effective oral cancer treatment.
    • Existing methods may lack the precision or automation required for efficient clinical application.

    Purpose of the Study:

    • To develop an automated deep convolutional neural network (DCNN) model for simultaneous oral cancer detection and region of interest (ROI) marking.
    • To enhance the precision of identifying cancerous regions by integrating texture analysis.

    Main Methods:

    • A novel texture-map-based branch-collaborative network (DCNN) was proposed, featuring two branches for detection and segmentation.
    • Texture images were extracted and analyzed using a sliding window to compute standard deviation values, forming a texture map.
    • The texture map was used as input for the DCNN model, which incorporated wavelet transform and Gabor filters.

    Main Results:

    • The DCNN model achieved an average sensitivity of 0.9687 and specificity of 0.7129 using wavelet transform.
    • Using Gabor filters, the model demonstrated an average sensitivity of 0.9314 and specificity of 0.9475.
    • The proposed method effectively detects cancerous regions and marks the ROI automatically.

    Conclusions:

    • The texture-map-based branch-collaborative network offers a promising automated solution for oral cancer detection and ROI marking.
    • The integration of texture analysis with DCNN improves the accuracy and precision of identifying cancerous tissues.
    • The model shows potential for clinical application in improving oral cancer diagnosis.