The accurate classification of the histopathological images of breast cancer diagnosis may face a huge challenge due to the complexity of the pathologist images. Currently, computer-aided diagnosis is implemented to get sound and error-less diagnosis of this lethal disease. However, the classification accuracy and processing time can be further improved. This study was designed to control diagnosis error via enhancing image accuracy and reducing processing time by applying several algorithms such as deep learning, K -means, autoencoder in clustering and enhanced loss function (ELF) in classification. Histopathological images were obtained from five datasets and pre-processed by using stain normalisation and linear transformation filter. These images were patched in sizes of 512 × 512 and 128 × 128 and extracted to preserve the tissue and cell levels to have important information of these images. The patches were further pre-trained by ResNet50-128 and ResNet512. Meanwhile, the 128 × 128 were clustered and autoencoder was employed with K -means which used latent feature of image to obtain better clustering result. Classification algorithm is used in current proposed system to ELF. This was achieved by combining SVM loss function and optimisation problem. The current study has shown that the deep learning algorithm has increased the accuracy of breast cancer classification up to 97% compared to state-of-the-art model which gave a percentage of 95%, and the time was decreased to vary from 30 to 40 s. Also, this work has enhanced system performance via improving clustering by employing K -means with autoencoder for the nonlinear transformation of histopathological image.

中文翻译：

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用于乳腺癌分类的深度卷积网络：增强损失函数（ELF）

由于病理学家图像的复杂性，乳腺癌诊断的组织病理学图像的准确分类可能面临巨大挑战。目前，实施计算机辅助诊断以对这种致命疾病进行可靠且无错误的诊断。但是，分类精度和处理时间可以进一步提高。本研究旨在通过应用深度学习、K 均值、聚类中的自动编码器和分类中的增强损失函数 (ELF) 等多种算法，通过提高图像准确性和减少处理时间来控制诊断错误。从五个数据集获得组织病理学图像，并通过使用染色归一化和线性变换过滤器进行预处理。这些图像以 512 × 512 和 128 × 128 的大小进行修补并提取以保留组织和细胞水平，以获取这些图像的重要信息。这些补丁由 ResNet50-128 和 ResNet512 进一步预训练。同时，对128×128进行聚类，并采用自编码器和K-means，利用图像的潜在特征获得更好的聚类结果。当前提出的系统中使用分类算法对ELF。这是通过结合 SVM 损失函数和优化问题来实现的。目前的研究表明，深度学习算法将乳腺癌分类的准确率提高了 97%，而最先进的模型给出了 95% 的百分比，时间从 30 到40 秒。还，