College of Applied Computer Sciences (CACS), Al-Muzahimiyah Branch, King Saud University, Riyadh, Saudi Arabia.
Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan, Tunisia.
Biomed Res Int. 2020 Feb 27;2020:4671349. doi: 10.1155/2020/4671349. eCollection 2020.
Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.
乳腺癌是全球女性中最常见的癌症。开发计算机辅助诊断工具对于帮助病理学家准确地解释和区分恶性和良性肿瘤至关重要。本文提出了一种基于机器学习算法的自动增殖性乳腺病变诊断方法。我们使用禁忌搜索来选择最重要的特征。特征的评估是基于粗糙集每个属性的依赖度。使用五个机器学习算法构建了简化特征的分类。所提出的模型应用于 BIDMC-MGH 和威斯康星州诊断乳腺癌数据集。使用五个标准评估了所使用模型的性能度量。表现最好的模型是 AdaBoost 和逻辑回归。与其他工作的比较证明了所提出的方法在诊断乳腺癌方面的效率优于已审查的分类技术。
