Department of ECE, Muthayammal Engineering College, Rasipuram, Namakkal, India.
Department of ECE, Mahendra College of Engineering, Salem, India.
J Med Syst. 2018 Oct 3;42(11):221. doi: 10.1007/s10916-018-1082-7.
Breast cancer is the important problem across the globe in which, most of the women are suffering without knowing the causes and effects of the cancer cells. Mammographic is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent techniques are suggested for the detection of Microcalcification, Clusters, Masses, Spiculate lesions, Asymmetry and Architectural distortions in the mammograms. But the prediction of the cancer levels needs more research light. For the determination of the higher level of accuracy and prediction, the proposed algorithm called Enhanced Gray Scale Adaptive Method (EGAM) which works on the principle of combination of K-GLCM and Extreme Fuzzy Learning Machines (EFLM). The proposed algorithm has achieved 99% accuracy and less computation time in terms of classification, detection and prediction when compared with the existing intelligent algorithms.
乳腺癌是全球范围内的一个重要问题，大多数女性在不知道癌细胞的原因和影响的情况下正在遭受痛苦。乳腺 X 线摄影是诊断乳腺癌的最有力工具。对这些乳腺 X 线照片的分析在诊断方面被证明更为重要，但准确性水平仍需要改进。已经提出了几种智能技术来检测乳腺 X 线片中的微钙化、簇状、肿块、刺状病变、不对称和结构扭曲。但是癌症水平的预测需要更多的研究。为了提高准确性和预测水平，提出了一种称为增强灰度自适应方法（EGAM）的算法，该算法基于 K-GLCM 和极端模糊学习机器（EFLM）的组合原理。与现有的智能算法相比，该算法在分类、检测和预测方面的准确率达到 99%，计算时间更少。
