In 2014, George et al. (2016a) in 2016. Comput. We used the Inception_V3 and Inception_ResNet_V2 networks to perform binary classification of histopathological images of breast cancer into benign and malignant tumors via transfer learning. The Inception_V3 (Szegedy et al., 2016) and Inception_ResNet_V2 (Szegedy et al., 2017) networks, proposed by Szegedy et al. (2018). To realize the development of a system for diagnosing breast cancer using multi-class classification on BreaKHis, Han et al. Compared to the results in Table 2, we can say that augmenting raw imbalanced breast cancer histopathological image datasets can greatly improve the reliability of the diagnosis system. 24, 1415–1422. The authors would like to thank Professor Spanhol et al. Breast cancer multi-classification from histopathological images with structured deep learning model. Multi-class classification studies on histopathological images of breast cancer can provide more reliable information for diagnosis and prognosis. This tells us that the breast cancer diagnosis system based on the augmented dataset and the Inception_ResNet_V2 network is very reliable. Advances in image processing and machine learning modes, in which CAD (Computer-Aided Diagnosis) systems are built, which helps pathologists to be more, objective and consistent in the diagnosis … The statistical significance between pairs of algorithms is displayed in the lower triangle using “*.”. 2020 Oct 20;34:140. doi: 10.34171/mjiri.34.140. Machine Learn. The experimental results on BreaKHis achieved the accuracy of 95.4%. The first several layers are characteristic transformation via the traditional convolutional layers and the pooling layers, and the middle part is composed of multiple Inception modules stacked together. Wei B, Han Z, He X, Yin Y. eCollection 2020. This analysis further demonstrates that the deep learning network Inception_ResNet_V2 has a powerful ability to extract informative features automatically. Front. Figure 5 compared the loss function of the Inception_ResNet_V2 network on the raw and extended datasets, respectively, for binary and multi-class classification of histopathological images of breast cancer. 2020 Sep 15;10:1151. doi: 10.3389/fonc.2020.01151. Therefore, we used Inception_ResNet_V2 to extract features from breast cancer histopathological images to perform unsupervised analysis of the images. It can be divided into six groups representing the following consistency levels: −1~0.0 (poor), 0.0~0.20 (slight), 0.21~0.40 (fair), 0.41~0.60 (moderate), 0.61~0.80 (substantial), and 0.81~1 (almost perfect) (Landis and Koch, 1977). This paper mainly help to predict cancer as malignant and benign. JAMA Oncol. A., and Soliman, T. H. A. The network structures of our proposed autoencoder and its combination with Inception_ResNet_V2, (A)…, The change in the loss function during the training of Inception_ResNet_V2 on raw…. The results are finally output through the fully-connected layer using the Softmax function. Stenkvist, B., Westman-Naeser, S., Holmquist, J., Nordin, B., Bengtsson, E., Vegelius, J., et al. How we can avoid or reduce the influence on the analysis of histopathological images of breast cancer from these issues will be the focus of our future work. Efficient diagnosis of cancer from histopathological images by eliminating batch effects. doi: 10.1109/TBME.2014.2303852. Deep learning-based approaches have recently gained popularity for analyzing histopathological images of human breast cancer. “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Las Vegas, NV). Figure 7. This system was tested on a database composed of 361 images, of which 119 were normal tissue, 102 were carcinoma in situ, and 140 were lobular carcinoma or invasive ductal. FN is the number of images incorrectly recognized as benign tumor in the testing subset. Transfer learning is adopted in this paper to classify the histopathological images of breast cancer using Inception_V3 and Inception_ResNet_V2 networks. Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (eds) (2016b). The number of neurons in the last fully-connected layer is set according to our specific task, and the parameters of the fully-connected layer are re-trained. It was reported that batch effects can lead to huge dissimilarities in features extracted from images (Mathews et al., 2016). IEEE Trans. Keywords: We calculate the criteria listed above in our experiments by calling functions embedded in the sklearn library (available as a Python package), such as silhouette_score (SSE), linear_assignment (ACC), adjusted_rand_score (ARI), and adjusted_mutual _info_score (AMI). The entire network is shown in Figure 4B. The Friedman's test results in Table 6 tell us that there is a strong significant difference between our approaches and the compared algorithms because any p in Table 6 supports p ≺ 0.05. The authors randomly split the images into training and testing sets, with 20% of each class' images used for testing and the rest used for training. 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