Abstract：
BACKGROUND: Breast cancer is one of the leading causes of mortality among women. In addition, 1 in 8 women and 1 in 833 men will be diagnosed with breast cancer in 2022. The detection of breast cancer can not only lower treatment costs but also increase survival rates. Due to increased cancer awareness, more women are undergoing breast cancer screening, leading to more cases being diagnosed worldwide, but doctors\' ability to analyze these images is limited. As a result, they get overloaded leading to misinterpretations. The advent of computer-aided diagnosis (CAD) minimized man\'s involvement and achieved good results. CAD helps medical doctors automatically detect and analyze abnormalities found in the breast. Such abnormalities may be benign or malignant tumors.
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.
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.
摘要：
背景：乳腺癌是女性死亡的主要原因之一。此外,1/8的女性和1/833的男性将在2022年被诊断出患有乳腺癌。检测乳腺癌不仅可以降低治疗成本，还可以提高生存率。由于对癌症的认识增加，越来越多的女性正在接受乳腺癌筛查，导致全世界更多的病例被诊断，但是医生分析这些图像的能力是有限的。因此,他们超负荷，导致误解。计算机辅助诊断（CAD）的出现最大限度地减少了人的参与，并取得了良好的效果。CAD帮助医生自动检测和分析乳房中发现的异常。这种异常可能是良性或恶性肿瘤。
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。
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。
