Recently, deep learning using convolutional neural network (CNN) has been used successfully to classify the images of breast cells accurately. However, the accuracy of manual classification of those histopathological images is comparatively low. This research aims to increase the accuracy of the classification of breast cancer images by utilizing a patch-based classifier (PBC) along with deep learning architecture. The proposed system consists of a deep convolutional neural network that helps in enhancing and increasing the accuracy of the classification process. This is done by the use of the PBC. CNN has completely different layers where images are first fed through convolutional layers using hyperbolic tangent function together with the max-pooling layer, drop out layers, and SoftMax function for classification. Further, the output obtained is fed to a PBC that consists of patch-wise classification output followed by majority voting. The results are obtained throughout the classification stage for breast cancer images that are collected from breast-histology datasets. The proposed solution improves the accuracy of classification whether or not the images had normal, benign, in-situ, or invasive carcinoma from 87% to 94% with a decrease in processing time from 0.45 to 0.2 s on average. The proposed solution focused on increasing the accuracy of classifying cancer in the breast by enhancing the image contrast and reducing the vanishing gradient. Finally, this solution for the implementation of the contrast limited adaptive histogram equalization technique and modified tangent function helps in increasing the accuracy.

中文翻译：

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乳腺癌分类的深度学习：增强的切线函数

最近，使用卷积神经网络 (CNN) 的深度学习已成功用于准确分类乳腺细胞的图像。然而，这些组织病理学图像的人工分类准确度相对较低。本研究旨在通过利用基于补丁的分类器 (PBC) 和深度学习架构来提高乳腺癌图像分类的准确性。所提出的系统由一个深度卷积神经网络组成，有助于增强和提高分类过程的准确性。这是通过使用 PBC 来完成的。CNN 具有完全不同的层，其中图像首先使用双曲正切函数以及最大池化层、丢弃层和 SoftMax 函数通过卷积层进行分类。更远，获得的输出被馈送到 PBC，该 PBC 由补丁分类输出和多数投票组成。从乳腺组织学数据集中收集的乳腺癌图像的整个分类阶段都获得了结果。所提出的解决方案将图像是否具有正常、良性、原位或浸润性癌的分类准确率从 87% 提高到 94%，平均处理时间从 0.45 秒减少到 0.2 秒。所提出的解决方案侧重于通过增强图像对比度和减少消失梯度来提高对乳房中癌症进行分类的准确性。最后，该解决方案用于实施对比度受限的自适应直方图均衡技术和修改的正切函数有助于提高准确性。