Balasubramaniam Sathiyabhama, Velmurugan Yuvarajan, Jaganathan Dhayanithi, Dhanasekaran Seshathiri
Computer Science and Engineering, Sona College of Technology, Salem 636005, India.
Department of Computer Science, UiT The Arctic University of Norway, 9037 Tromso, Norway.
Diagnostics (Basel). 2023 Aug 24;13(17):2746. doi: 10.3390/diagnostics13172746.
Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the "dying ReLU" problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection.
卷积神经网络（CNN）已被广泛应用于医学图像处理，以自动提取有意义的特征并对各种医疗状况进行分类，从而实现更快、更准确的诊断。在本文中，经典的CNN架构LeNet已成功应用于乳腺癌数据分析。它展示了其提取判别性特征并高精度地对恶性和良性肿瘤进行分类的能力，从而支持乳腺癌的早期检测和诊断。带有修正线性整流单元（ReLU）的LeNet，这是对传统ReLU激活函数的一种改进，已被发现通过解决“死亡ReLU”问题并增强提取特征的判别力，在乳腺癌数据分析任务中提高了LeNet的性能。这导致了更准确、可靠的乳腺癌检测和诊断，并改善了患者的治疗结果。批量归一化提高了像LeNet这样的小型浅层CNN架构的性能和训练稳定性。它有助于减轻内部协变量偏移的影响，内部协变量偏移是指训练期间网络激活分布的变化。这个分类器将减轻过拟合问题并减少运行时间。所设计的分类器与基准深度学习模型进行了评估，证明其产生了更高的识别率。乳腺图像识别率的准确率为89.91%。该模型将在分割、特征提取、分类和乳腺癌肿瘤检测方面取得更好的性能。
