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A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment

机译:一部新颖的事物互联网框架与智能医疗环境的实时监控集成

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摘要

During mammogram screening, there is a higher probability that detection of cancers is missed, and more than 16 percentage of breast cancer is not detected by radiologists. This problem can be solved by employing image processing algorithms which enhances the accuracy of the diagnostic through image segmentation which reduces the misclassified malignant cancers. By employing segmentation, the unnecessary regions in the breast close to the boundary between the breast tissue and segmented pectoral muscle can be removed, therefore enhancing the accuracy the calculation as well as feature estimation. In-order to enhance the accuracy of classification, the proposed classifier integrates the decision trees and neural network into a system to report the progress of the breast cancer patients in an appropriate manner with the help of technology used in healthcare system. The proposed classifier successfully demonstrated that it achieved more accurate prediction when compared with other widely used algorithms, namely, K-Nearest Neighbors, Support Vector Machine and Naive Bayes algorithm.
机译:在乳房X线照片筛选期间,存在癌症的检测较高的概率,并且放射科医师未检测到超过16个百分比的乳腺癌。通过采用图像处理算法可以解决该问题,该图像处理算法通过图像分割来增强诊断的准确性,这减少了错误分类的恶性癌症。通过采用分割,可以去除乳房之间的乳房和分段胸肌之间的边界的不必要区域,因此提高了计算的准确性以及特征估计。为了提高分类的准确性,所提出的分类器将决策树和神经网络集成到系统中,以在医疗系统中使用的技术的帮助下以适当的方式以适当的方式报告乳腺癌患者的进度。该提议的分类器成功地证明,与其他广泛使用的算法相比,即K-Collect邻居,支持向量机和幼稚贝叶斯算法相比,它达到了更准确的预测。

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