R-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration
Rokonozzaman Ayon, Md Taimur Ahad, Bo Song, Yan Li
最先进的(SOTA)卷积神经网络(CNN)因其广泛的计算能力,长时间的训练时间和大型数据集而受到批评。 为了克服这一限制,我们提出了一个合理的网络(R-Net),一个轻量级的CNN,仅使用肠镜活检组织病理学血氧林和Eosin图像数据集(EBHI)检测和分类结直肠癌(CRC)。 此外,6个SOTA CNN,包括基于多路径的CNN(DenseNet121,ResNet50),基于深度的CNN(InceptionV3),基于宽度的多连接CNN(Xception),深度可分离卷积(MobileNetV2),基于空间利用的CNN(VGG16),传输学习和两个集成模型也在同一数据集上进行测试。 集成模型是多路径深度-带宽组合(DenseNet121-InceptionV3-Xception)和多路径深度空间组合(ResNet18-InceptionV3-VGG16)。 然而,拟议的R-Net轻量级实现了99.37
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 (Inception...
