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<p>ColoRectal Cancer (CRC) is an increasing worldwide health concern, marked by persistently high mortality rates. Early detection is crucial for increasing life expectancy and reducing the risk of illness. Conventional visual inspection of biopsy slides stained with haematoxylin and eosin is time-consuming and subjective. Microscopic investigation or naked-eye inspection by clinical experts is always suboptimal. Moreover, this approach requires extensive and high-cost genetic testing. Offering a new paradigm, with a recent advancement of image processing and machine learning techniques, we propose a robust CRC subtype classification framework called “DC3NET” (abbreviated as Depthwise separable Convolutional Colorectal Cancer Neural Network). DiSC (Depthwise Separable Convolutional Neural Network) is used for extracting hidden morphological features from histopathological images. Then, the multiclass classification is performed to distinguish the CRC subtypes using an Optimized Support Vector Machine. Meta-visualisation techniques such as GradCAM, occlusion sensitivity, gradient attribution are also integrated to improve clinical interpretability. The evaluation metrics such as sensitivity, accuracy, precision, F1 score are evaluated. Our proposed DC3NET model resulted in an impressive validation accuracy of 94.92% and 99% on the Kather and NCT-CRC-HE-100&#xa0;K colorectal histology datasets. A comparative analysis was carried out with all variants of SVM and our proposed DC3NET surpasses other models.</p>

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Next-gen colorectal cancer diagnosis using depthwise separable convolution in histopathology image analysis

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • G. Pooja,
  • P. Hariharan,
  • N. Sasikaladevi,
  • K. Geetha,
  • Rengarajan Amirtharajan

摘要

ColoRectal Cancer (CRC) is an increasing worldwide health concern, marked by persistently high mortality rates. Early detection is crucial for increasing life expectancy and reducing the risk of illness. Conventional visual inspection of biopsy slides stained with haematoxylin and eosin is time-consuming and subjective. Microscopic investigation or naked-eye inspection by clinical experts is always suboptimal. Moreover, this approach requires extensive and high-cost genetic testing. Offering a new paradigm, with a recent advancement of image processing and machine learning techniques, we propose a robust CRC subtype classification framework called “DC3NET” (abbreviated as Depthwise separable Convolutional Colorectal Cancer Neural Network). DiSC (Depthwise Separable Convolutional Neural Network) is used for extracting hidden morphological features from histopathological images. Then, the multiclass classification is performed to distinguish the CRC subtypes using an Optimized Support Vector Machine. Meta-visualisation techniques such as GradCAM, occlusion sensitivity, gradient attribution are also integrated to improve clinical interpretability. The evaluation metrics such as sensitivity, accuracy, precision, F1 score are evaluated. Our proposed DC3NET model resulted in an impressive validation accuracy of 94.92% and 99% on the Kather and NCT-CRC-HE-100 K colorectal histology datasets. A comparative analysis was carried out with all variants of SVM and our proposed DC3NET surpasses other models.