Wisaeng Kittipol
Technology and Business Information System Unit, Mahasarakham Business School, Mahasarakham University, Mahasarakham 44150, Thailand.
Diagnostics (Basel). 2022 Dec 7;12(12):3088. doi: 10.3390/diagnostics12123088.
Traditional breast cancer detection algorithms require manual extraction of features from mammogram images and professional medical knowledge. Still, the quality of mammogram images hampers this and extracting high-quality features, which can result in very long processing times. Therefore, this paper proposes a new K-means++ clustering based on Cuckoo Search Optimization (KM++CSO) for breast cancer detection. The pre-processing method is used to improve the proposed KM++CSO method more segmentation efficiently. Furthermore, the interpretability is further enhanced using mathematical morphology and OTSU's threshold. To this end, we tested the effectiveness of the KM++CSO methods on the mammogram image analysis society of the Mini-Mammographic Image Analysis Society (Mini-MIAS), the Digital Database for Screening Mammography (DDSM), and the Breast Cancer Digital Repository (BCDR) dataset through cross-validation. We maximize the accuracy and Jaccard index score, which is a measure that indicates the similarity between detected cancer and their corresponding reference cancer regions. The experimental results showed that the detection method obtained an accuracy of 96.42% (Mini-MIAS), 95.49% (DDSM), and 96.92% (BCDR). On overage, the KM++CSO method obtained 96.27% accuracy for three publicly available datasets. In addition, the detection results provided the 91.05% Jaccard index score.
传统的乳腺癌检测算法需要从乳房X光图像中手动提取特征并具备专业医学知识。然而，乳房X光图像的质量对此造成了阻碍，并且提取高质量特征可能会导致很长的处理时间。因此，本文提出了一种基于布谷鸟搜索优化的新型K均值++聚类算法（KM++CSO）用于乳腺癌检测。预处理方法用于更高效地分割，从而改进所提出的KM++CSO方法。此外，使用数学形态学和大津阈值进一步增强了可解释性。为此，我们通过交叉验证在Mini-Mammographic Image Analysis Society（Mini-MIAS）的乳房X光图像分析协会、数字乳腺筛查数据库（DDSM）以及乳腺癌数字库（BCDR）数据集上测试了KM++CSO方法的有效性。我们最大化准确率和杰卡德指数得分，杰卡德指数得分是一种衡量检测到的癌症与其相应参考癌症区域之间相似度的指标。实验结果表明，该检测方法在Mini-MIAS数据集上的准确率为96.42%，在DDSM数据集上为95.49%，在BCDR数据集上为96.92%。平均而言，KM++CSO方法在三个公开可用数据集上的准确率为96.27%。此外，检测结果的杰卡德指数得分为91.05%。
