Volunteer computing based data processing is a new trend in healthcare applications. Researchers are now leveraging volunteer computing power to train deep learning networks consisting of billions of parameters. Breast cancer is the second most common cause of death in women among cancers. The early detection of cancer may diminish the death risk of patients. Since the diagnosis of breast cancer manually takes lengthy time and there is a scarcity of detection systems, development of an automatic diagnosis system is needed for early detection of cancer. Machine learning models are now widely used for cancer detection and prediction research for improving the successive therapy of patients. Considering this need, this study implements pre-trained convolutional neural network based models for detecting breast cancer using ultrasound images. In particular, we tuned the pre-trained models for extracting key features from ultrasound images and included a classifier on the top layer. We measured accuracy of seven popular state-of-the-art pre-trained models using different optimizers and hyper-parameters through fivefold cross validation. Moreover, we consider Grad-CAM and occlusion mapping techniques to examine how well the models extract key features from the ultrasound images to detect cancers. We observe that after fine tuning, DenseNet201 and ResNet50 show 100% accuracy with Adam and RMSprop optimizers. VGG16 shows 100% accuracy using the Stochastic Gradient Descent optimizer. We also develop a custom convolutional neural network model with a smaller number of layers compared to large layers in the pre-trained models. The model also shows 100% accuracy using the Adam optimizer in classifying healthy and breast cancer patients. It is our belief that the model will assist healthcare experts with improved and faster patient screening and pave a way to further breast cancer research.

中文翻译：

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使用超声图像检测乳腺癌的预训练卷积神经网络

基于志愿计算的数据处理是医疗保健应用的新趋势。研究人员现在正在利用志愿者计算能力来训练由数十亿个参数组成的深度学习网络。乳腺癌是癌症中女性死亡的第二大常见原因。癌症的早期发现可能会降低患者的死亡风险。由于人工诊断乳腺癌耗时长，检测系统匮乏，因此需要开发一种自动诊断系统来早期发现癌症。机器学习模型现在广泛用于癌症检测和预测研究，以改善患者的后续治疗。考虑到这一需求，本研究实施了基于预训练卷积神经网络的模型，用于使用超声图像检测乳腺癌。特别是，我们调整了用于从超声图像中提取关键特征的预训练模型，并在顶层包括了一个分类器。我们通过五重交叉验证使用不同的优化器和超参数测量了七种流行的最先进的预训练模型的准确性。此外，我们考虑使用 Grad-CAM 和遮挡映射技术来检查模型从超声图像中提取关键特征以检测癌症的能力。我们观察到，经过微调后，DenseNet201 和 ResNet50 使用 Adam 和 RMSprop 优化器显示出 100% 的准确率。VGG16 使用随机梯度下降优化器显示 100% 的准确度。与预训练模型中的大层相比，我们还开发了一个层数较少的自定义卷积神经网络模型。该模型还显示了使用 Adam 优化器对健康和乳腺癌患者进行分类的 100% 准确度。我们相信，该模型将帮助医疗保健专家进行改进和更快的患者筛查，并为进一步的乳腺癌研究铺平道路。