Narasimha Raju Akella S, Jayavel Kayalvizhi, Rajalakshmi T
Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, 603203 Chennai, India.
Department of Electronics and Communication Engineering, School of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203 Chennai, India.
Comput Math Methods Med. 2022 Nov 10;2022:8723957. doi: 10.1155/2022/8723957. eCollection 2022.
Colorectal cancer typically affects the gastrointestinal tract within the human body. Colonoscopy is one of the most accurate methods of detecting cancer. The current system facilitates the identification of cancer by computer-assisted diagnosis (CADx) systems with a limited number of deep learning methods. It does not imply the depiction of mixed datasets for the functioning of the system. The proposed system, called ColoRectalCADx, is supported by deep learning (DL) models suitable for cancer research. The CADx system comprises five stages: convolutional neural networks (CNN), support vector machine (SVM), long short-term memory (LSTM), visual explanation such as gradient-weighted class activation mapping (Grad-CAM), and semantic segmentation phases. Here, the key components of the CADx system are equipped with 9 individual and 12 integrated CNNs, implying that the system consists mainly of investigational experiments with a total of 21 CNNs. In the subsequent phase, the CADx has a combination of CNNs of concatenated transfer learning functions associated with the machine SVM classification. Additional classification is applied to ensure effective transfer of results from CNN to LSTM. The system is mainly made up of a combination of CVC Clinic DB, Kvasir2, and Hyper Kvasir input as a mixed dataset. 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.
结直肠癌通常影响人体的胃肠道。结肠镜检查是检测癌症最准确的方法之一。当前系统通过使用有限数量深度学习方法的计算机辅助诊断（CADx）系统来促进癌症识别。这并不意味着为系统运行描绘混合数据集。所提出的名为ColoRectalCADx的系统由适用于癌症研究的深度学习（DL）模型支持。CADx系统包括五个阶段：卷积神经网络（CNN）、支持向量机（SVM）、长短期记忆（LSTM）、诸如梯度加权类激活映射（Grad-CAM）的视觉解释以及语义分割阶段。在此，CADx系统的关键组件配备有9个单独的和12个集成的CNN，这意味着该系统主要由总共21个CNN的研究实验组成。在随后的阶段，CADx具有与机器SVM分类相关联的级联迁移学习功能的CNN组合。应用额外的分类以确保结果从CNN有效转移到LSTM。该系统主要由作为混合数据集输入的CVC Clinic DB、Kvasir2和Hyper Kvasir组合而成。在CNN和LSTM之后，在高级阶段，通过使用具有Grad-CAM的更好的息肉识别技术和使用U-Net的语义分割来检测恶性肿瘤。CADx结果已存储在谷歌云以进行记录保存。在这些实验中，在所有CNN中，单独的CNN DenseNet-201（训练准确率87.1%，测试准确率84.7%）和集成的CNN ADaDR-22（训练准确率84.61%，测试准确率82.17%）对于使用CNN+LSTM模型进行癌症检测是最有效的。ColoRectalCADx通过单独的CNN DesnseNet-201和集成的CNN ADaDR-22准确识别癌症。在Grad-CAM的视觉解释中，CNN DenseNet-201显示息肉的精确可视化，而CNN U-Net提供精确的恶性息肉。
