OBJECTIVE: The goal of this study is to evaluate the effectiveness of using seven layers to classify breast cancer as either benign or malignant using mammograms.
METHODS: The open-source MIAS dataset of 322 images was used for our study, of which 207 were normal images and 115 were abnormal images. The proposed CNN model convolves an image into seven layers that extract features from the input images, and these features are used to classify breast cancer as malignant or benign.
RESULTS: The proposed CNN used a limited data set and achieved the best result compared to previous work. The method achieved results with a 0.39% loss, 99.89% accuracy, 99.85% precision, 99.89% recall, 99.87% F1-score, and an area under the curve noted to be 100.0%.
CONCLUSIONS: CNN uses a small amount of data to determine abnormalities; the method will assist a medical doctor in determining whether or not a specific patient has cancer.
目的:本研究的目的是评估使用7层乳房X光检查将乳腺癌分类为良性或恶性的有效性。
方法:我们使用了322张图像的开源MIAS数据集,其中正常图像207张,异常图像115张。提出的CNN模型将图像卷积为七层,从输入图像中提取特征,这些特征用于将乳腺癌分类为恶性或良性。
结果:提出的CNN使用了有限的数据集,与以前的工作相比取得了最好的结果。该方法获得的结果损失为0.39%,99.89%的准确度,99.85%精度,99.89%召回,99.87%F1分数,曲线下面积为100.0%。
结论:CNN使用少量数据来确定异常;该方法将帮助医生确定特定患者是否患有癌症。