School of Computer Science, MIT World Peace University, Pune, India.
Model Institute of Engineering and Technology, Jammu, J&K, India.
Biomed Res Int. 2023 Jan 31;2023:6970256. doi: 10.1155/2023/6970256. eCollection 2023.
The application of computational approaches in medical science for diagnosis is made possible by the development in technical advancements connected to computer and biological sciences. The current cancer diagnosis system is becoming outmoded due to the new and rapid growth in cancer cases, and new, effective, and efficient methodologies are now required. Accurate cancer-type prediction is essential for cancer diagnosis and treatment. Understanding, diagnosing, and identifying the various types of cancer can be greatly aided by knowledge of the cancer genes. The Convolution Neural Network (CNN) and neural pattern recognition (NPR) approaches are used in this study paper to detect and predict the type of cancer. Different Convolution Neural Networks (CNNs) have been proposed by various researchers up to this point. Each model concentrated on a certain set of parameters to simulate the expression of genes. We have developed a novel CNN-NPR architecture that predicts cancer type while accounting for the tissue of origin using high-dimensional gene expression inputs. The 5000-person sample of the 1-D CNN integrated with NPR is trained and tested on the gene profile, mapping with various cancer kinds. The proposed model's accuracy of 94% suggests that the suggested combination may be useful for long-term cancer diagnosis and detection. Fewer parameters are required for the suggested model to be efficiently trained before prediction.
计算方法在医学诊断中的应用得益于与计算机和生物科学相关的技术进步。由于癌症病例的新的快速增长，当前的癌症诊断系统已经过时，现在需要新的、有效和高效的方法。准确的癌症类型预测对于癌症的诊断和治疗至关重要。了解、诊断和识别各种类型的癌症可以极大地受益于癌症基因的知识。卷积神经网络（CNN）和神经模式识别（NPR）方法用于本研究论文中检测和预测癌症类型。到目前为止，不同的研究人员已经提出了不同的卷积神经网络（CNN）。每个模型都集中在一组特定的参数上，以模拟基因的表达。我们开发了一种新的 CNN-NPR 架构，该架构使用高维基因表达输入来预测癌症类型，同时考虑到起源组织。在基因图谱上对 5000 人的 1-D CNN 与 NPR 集成样本进行训练和测试，与各种癌症类型进行映射。所提出模型的 94%的准确率表明，所提出的组合可能对长期癌症诊断和检测有用。建议的模型在预测之前需要更少的参数进行有效训练。
