Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, T.M.Palayam, Coimbatore, TamilNadu, 641105, India.
Department of Computer Science and Engineering, Bharathidasan University, Trichy, Tiruchirappalli, India.
J Med Syst. 2019 May 3;43(6):165. doi: 10.1007/s10916-019-1302-9.
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.
在乳房 X 光筛查期间，存在更高的可能性会错过癌症的检测，超过 16%的乳腺癌无法被放射科医生检测到。这个问题可以通过使用图像处理算法来解决，这些算法通过图像分割来提高诊断的准确性，从而减少错误分类的恶性癌症。通过使用分割，可以去除乳房中靠近乳房组织和分割的胸肌之间边界的不必要区域，从而提高计算和特征估计的准确性。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一个系统中，借助医疗保健系统中使用的技术，以适当的方式报告乳腺癌患者的进展情况。所提出的分类器成功地证明，与其他广泛使用的算法（即 K-最近邻、支持向量机和朴素贝叶斯算法）相比，它实现了更准确的预测。
