TY -的A2 -罗伊,Sudipta盟——Narasimha Raju Akella s . AU - Jayavel Kayalvizhi盟——Rajalakshmi t . PY - 2022 DA - 2022/11/10 TI - ColoRectalCADx:迅速识别结直肠癌的综合卷积神经网络和视觉解释使用混合数据集证据SP - 8723957六世- 2022 AB -结肠直肠癌通常会影响人体胃肠道内。结肠镜检查是检测癌症的最精确的方法之一。当前系统便于识别计算机辅助诊断的癌症(CADx)系统与数量有限的深度学习的方法。这并不意味着混合数据集的描述系统的功能。支持拟议的系统,称为ColoRectalCADx,深度学习(DL)模型适合癌症研究。CADx系统包括五个阶段:卷积神经网络(CNN),支持向量机(SVM),长期短期记忆(LSTM),视觉解释如gradient-weighted类激活映射(Grad-CAM)和语义细分阶段。这里,CADx系统的关键组件配有9个人和12集成cnn,这意味着系统主要由临床实验的实验共有21 cnn。在随后的阶段,CADx cnn的组合连接传输功能与支持向量机的机器学习分类。额外的分类应用,以确保有效的结果从CNN LSTM转移。系统主要是由一个组合的CVC诊所DB, Kvasir2,超级Kvasir输入为一个复杂的数据集。 After CNN and LSTM, in advanced stage, malignancies are detected by using a better polyp recognition technique with Grad-CAM and semantic segmentation using U-Net. CADx results have been stored on Google Cloud for record retention. In these experiments, among all the CNNs, the individual CNN DenseNet-201 (87.1% training and 84.7% testing accuracies) and the integrated CNN ADaDR-22 (84.61% training and 82.17% testing accuracies) were the most efficient for cancer detection with the CNN+LSTM model. ColoRectalCADx accurately identifies cancer through individual CNN DesnseNet-201 and integrated CNN ADaDR-22. In Grad-CAM’s visual explanations, CNN DenseNet-201 displays precise visualization of polyps, and CNN U-Net provides precise malignant polyps. SN - 1748-670X UR - https://doi.org/10.1155/2022/8723957 DO - 10.1155/2022/8723957 JF - Computational and Mathematical Methods in Medicine PB - Hindawi KW - ER -