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首页> 外文期刊>American journal of applied sciences >A Novel Approach in Malignancy Detection of Computer Aided Diagnosis | Science Publications
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A Novel Approach in Malignancy Detection of Computer Aided Diagnosis | Science Publications

机译：计算机辅助诊断恶性肿瘤的新方法科学出版物

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> Problem statement: Breast cancer is one of the most dangerous diseases that cause innumerable fatal in the female society. Early detection is the only way to reduce the mortality. Due to variety of factors sometimes manual reading of mammogram results in misdiagnosis. So that the diagnosis rate varies from 65-85%. Various computer aided detection techniques have been proposed for the past 20 years. Even then the detection rate is still not high. Approach: The proposed method consists of the following steps preprocessing, segmentation, feature extraction and classification. Noise, Artifact and pectoral region are removed in a preprocessing step. Contrast enhancement and Sobel operator with segmentation algorithm is used to segment the mass region. Feature extraction is performed on the segmented image using gray level co-occurrence matrix and local binary pattern method. Extracted features are classified using support vector machine. The performance of the proposed system is evaluated using partest method. Results: Proposed algorithm shows 98.8% sensitivity and 97.4% Specificity. Conclusion: The proposed algorithm is fully automatic and will be helpful in assisting the radiologists to detect the malignancy efficiently.
机译： > 问题陈述：乳腺癌是导致女性社会无数致命性的最危险疾病之一。早期发现是降低死亡率的唯一方法。由于各种因素，有时手动读取乳房X线照片会导致误诊。因此诊断率从65％到85％不等。在过去的20年中，已经提出了各种计算机辅助检测技术。即使这样，检测率仍然不高。 方法：所提出的方法包括以下步骤：预处理，分割，特征提取和分类。在预处理步骤中，将噪声，伪影和胸部区域除去。对比度增强和带分割算法的Sobel算子用于分割质量区域。使用灰度共生矩阵和局部二进制模式方法对分割后的图像进行特征提取。使用支持向量机对提取的特征进行分类。提出的系统的性能是使用部分方法进行评估的。 结果：提出的算法显示出98.8％的敏感性和97.4％的特异性。 结论：提出的算法是全自动的，将有助于协助放射科医生有效地检测恶性肿瘤。

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