Bharath E, Raja R Vimal, Kalaivanan K, Deshpande Vivek
Department of Artificial Intelligence and Data Science, CK College of Engineering and Technology, Cuddalore, Tamil Nadu, India.
Department of Computer Science and Engineering, CK College of Engineering and Technology, Cuddalore, Tamil Nadu, India.
Abdom Radiol (NY). 2025 Jan 8. doi: 10.1007/s00261-024-04770-2.
Colorectal cancer (CRC) is one of the most common and deadly forms of cancer worldwide, necessitating accurate and early detection to improve treatment outcomes. Traditional diagnostic methods often rely on manual examination of pathological images, which can be time-consuming and prone to human error. This study presents an advanced approach for colorectal cancer detection using a Random Hinge Exponential Distribution coupled Attention Network (RHED-CANet) on pathological images. The input dataset is sourced from the TCGA-CRC-DX cohort and the CRC dataset, both widely recognized for their comprehensive coverage of colorectal cancer cases. Pre-processing and feature extraction are performed using a Modified Square Root Sage-Husa Adaptive Kalman Filter combined with a Spike-Driven Transformer, enhancing noise reduction and feature clarity. Segmentation is achieved through an EfficientNetV2L Inception Transformer, ensuring precise delineation of cancerous regions. The final classification utilizes the RHED-CANet, a network tailored to handle the complexities of pathological data with high accuracy. This methodology achieved remarkable results, with an accuracy of 99.9% and a precision of 99.7%. These performance metrics underscore the method's ability to minimize false positives and enhance diagnostic accuracy. The proposed approach offers significant advantages, including a reduction in diagnostic time and a substantial improvement in detection accuracy, making it a promising tool for clinical applications. Despite its excellent accuracy, the suggested RHED-CANet technique has drawbacks, such as overfitting the TCGA-CRC-DX and CRC datasets by reducing generalizability on other datasets comprising other cancer types or image qualities. The actual application of the techniques in real-time clinical applications may be hampered by this computational load, especially in settings with limited resources, and the model's potential computational complexity due to multiple advanced processing steps. Additionally, the efficiency of training may be impacted by biased inputs, particularly for minor CRC subtypes.
结直肠癌（CRC）是全球最常见且致命的癌症形式之一，需要准确的早期检测以改善治疗效果。传统的诊断方法通常依赖于对病理图像的人工检查，这可能既耗时又容易出现人为错误。本研究提出了一种先进的方法，用于在病理图像上使用随机铰链指数分布耦合注意力网络（RHED-CANet）进行结直肠癌检测。输入数据集来自TCGA-CRC-DX队列和CRC数据集，这两个数据集因其对结直肠癌病例的全面覆盖而广受认可。使用改进的平方根Sage-Husa自适应卡尔曼滤波器结合脉冲驱动变压器进行预处理和特征提取，增强了降噪和特征清晰度。通过EfficientNetV2L Inception变压器实现分割，确保对癌性区域进行精确描绘。最终分类使用RHED-CANet，这是一个专门设计用于高精度处理病理数据复杂性的网络。该方法取得了显著成果，准确率为99.9%，精确率为99.7%。这些性能指标强调了该方法将假阳性降至最低并提高诊断准确性的能力。所提出的方法具有显著优势，包括减少诊断时间和大幅提高检测准确性，使其成为临床应用中有前景的工具。尽管其准确率极高，但所建议的RHED-CANet技术存在缺点，例如通过降低在包含其他癌症类型或图像质量的其他数据集上的通用性而过度拟合TCGA-CRC-DX和CRC数据集。这种计算负担可能会阻碍该技术在实时临床应用中的实际应用，特别是在资源有限的环境中，以及由于多个先进处理步骤导致模型潜在的计算复杂性。此外，训练效率可能会受到有偏差输入的影响，特别是对于较小的CRC亚型。
