Abstract
State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) are criticized for their extensive computational power, long training times, and large datasets. To overcome this limitation, we propose a reasonable network (R-Net), a lightweight CNN only to detect and classify colorectal cancer (CRC) using the Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset (EBHI). Furthermore, six SOTA CNNs, including Multipath-based CNNs (DenseNet121, ResNet50), Depth-based CNNs (InceptionV3), width-based multi-connection CNNs (Xception), depth-wise separable convolutions (MobileNetV2), spatial exploitation-based CNNs (VGG16), Transfer learning, and two ensemble models are also tested on the same dataset. The ensemble models are a multipath-depth-width combination (DenseNet121-InceptionV3-Xception) and a multipath-depth-spatial combination (ResNet18-InceptionV3-VGG16). However, the proposed R-Net lightweight achieved 99.37% accuracy, outperforming MobileNet (95.83%) and ResNet50 (96.94%). Most importantly, to understand the decision-making of R-Net, Explainable AI such as SHAP, LIME, and Grad-CAM are integrated to visualize which parts of the EBHI image contribute to the detection and classification process of R-Net. The main novelty of this research lies in building a reliable, lightweight CNN R-Net that requires fewer computing resources yet maintains strong prediction results. SOTA CNNs, transfer learning, and ensemble models also extend our knowledge on CRC classification and detection. XAI functionality and the impact of pixel intensity on correct and incorrect classification images are also some novelties in CRC detection and classification.
Abstract (translated)
最先进的（SOTA）卷积神经网络（CNN）因其计算能力要求高、训练时间长和需要大量数据而受到批评。为克服这一限制，我们提出了一种合理的网络（R-Net），这是一种轻量级的CNN，用于使用肠镜活检组织病理学苏木精和伊红图像数据集（EBHI）检测和分类结直肠癌（CRC）。此外，还测试了六个SOTA CNN模型，包括基于多路径的CNNs（DenseNet121、ResNet50）、基于深度的CNNs（InceptionV3）、基于宽度的多连接CNNs（Xception）、深度可分离卷积（MobileNetV2）、空间探索型CNNs（VGG16）以及迁移学习和两个集成模型。这些集成模型分别是多路径-深度-宽度组合（DenseNet121-InceptionV3-Xception）和多路径-深度-空间组合（ResNet18-InceptionV3-VGG16）。然而，提出的轻量级R-Net达到了99.37%的准确率，超过了MobileNet（95.83%）和ResNet50（96.94%）。尤为重要的是，为了理解R-Net的决策过程，我们集成了可解释的人工智能技术如SHAP、LIME和Grad-CAM来可视化EBHI图像中哪些部分对R-Net的检测和分类过程有贡献。 这项研究的主要创新之处在于构建了一个可靠且轻量级的CNN R-Net，它需要较少的计算资源但保持了强大的预测性能。SOTA CNNs、迁移学习以及集成模型也扩展了我们对CRC分类和检测的理解。XAI功能（如解释性人工智能）以及像素强度对正确与错误分类图像的影响也是CRC检测和分类的一些新特点。
URL
https://arxiv.org/abs/2509.16251
