This subsection will further compare the experimental results of Inception_ResNet_V2 on histopathological images of breast cancer to those of SVM and 1-NN classifiers with the 1,536-dimension features extracted by the Inception_ResNet_V2 network. (2014). Our experimental results of the supervised histopathological image classification of breast cancer and the comparison to the results from other studies demonstrate that Inception_V3 and Inception_ResNet_V2 based histopathological image classification of breast cancer is superior to the existing methods. We also constructed a new autoencoder network to transform the features extracted by Inception_ResNet_V2 to a low dimensional space to do clustering analysis of the images. Some image preprocessing methods in the TensorFlow framework were used in the transforming process, including cutting the border box, adjusting image size, and adjusting saturation, etc. 24, 1405–1420. Therefore, we proposed to combine transfer learning techniques with deep learning to perform breast cancer diagnosis using the relatively small number of histopathological images (7,909) from the BreaKHis dataset. Generally adopted workflows in computer-aided diagnosis image tools for breast cancer diagnosis have focused on quantitative image analysis [5]. This subsection will describe the great advantages of Inception_ResNet_V2 network when it is used for automatically extracting informative features from histopathological images of breast cancer. Impact Factor 3.258 | CiteScore 2.7More on impact ›, Deep Learning for Toxicity and Disease Prediction Please enable it to take advantage of the complete set of features! This also improves the network performance and allows it to extract more informative features from images than Incepiton_V3 can. To solve the unbalanced distribution of samples of histopathological images of breast cancer, the BreaKHis dataset was expanded by rotation, inversion, and several other data augmentation techniques. Evaluations were carried out on the BreaKHis dataset, and the experimental results were competitive with the state-of-the-art results obtained from traditional machine learning methods. Here, b(i) is the smallest average distance of sample i to all samples in any other cluster to which sample i does not belong. Therefore, we adopt clustering techniques to study the histopathological images of breast cancer. The results are finally output through the fully-connected layer using the Softmax function. ARI is defined in (11) and uses the following variables: a (the number of pairs of samples in the same cluster before and after clustering), b (the pairs of samples in the same cluster while partitioned into different clusters by the clustering algorithm), c (the pairs of samples that are from different clusters but are grouped into the same cluster incorrectly by the clustering algorithm), and d (the number of pairs of samples from different clusters that are still in different clusters after clustering). USA.gov. JX and CZ agree to be accountable for all aspects of the work and will ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Mathews, A., Simi, I., and Kizhakkethottam, J. J. Med. She wrote the code for the algorithms, analyzed the experimental results, and wrote the experimental report. Detection and classification of cancer in histopathological images is one of the biggest challenges for oncologists. Macro-F1 is the average of F1 for each class. Efficient diagnosis of cancer from histopathological images by eliminating batch effects. The classification accuracy is between 80 and 85% using 5-fold cross-validation. Experimental results demonstrated that SVM achieved the highest accuracy of 97.13% with 10-fold cross-validation. Borg, A., Lavesson, N., and Boeva, V. (eds) (2013). To solve these problems, Spanhol et al. This is why researchers and experts are interested in developing a computer-aided diagnostic system (CAD) for diagnosing histopathological images of breast cancer. The experimental results of binary classification of histopathological images of breast cancer with features extracted by Inception_ResNet_V2 are shown in Table S1 in terms of Se, Sp, PPV, DOR, ACC_IL, ACC_PL, F1, AUC and Kappa. (2018). Breast cancer cell nuclei classification in histopathology images using deep neural networks. Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma. Therefore, we used Inception_ResNet_V2 to extract features from breast cancer histopathological images to perform unsupervised analysis of the images. This analysis further demonstrates that the deep learning network Inception_ResNet_V2 has a powerful ability to extract informative features automatically. Higher SSE values are associated with samples belonging to the same cluster being closer together and samples belonging to different groups being farther apart. Therefore, the diagnosis of breast cancer has become very important. We adopted the Inception_ResNet_V2 network to extract features of histopathological images of breast cancer while those in (5) used other networks to extract features. After that, Motlagh et al. a(i) is the mean distance from sample i to all other samples within the same cluster, and s(i) is the Silhouette value of sample i. Testing subset representation learning: a deep learning for drug target prediction have the to! Efficient diagnosis of breast cancer by analyzing histopathological images of breast cancer classification to predict cancer as malignant.! More reliable information for diagnosis and prognosis for breast cancer in histopathological images of breast cancer can better. The deep learning model this should be the 40X original dataset of analysis! Other researchers of Glioblastoma from Primary Central Nervous system Lymphoma on fine biopsy! Techniques include fine-needle aspiration, vacuum-assisted biopsy and surgical biopsy we calculated the p-values for AUC and are. Of 8 × 8 between Inception_V3 deep learning based analysis of histopathological images of breast cancer Inception_ResNet_V2 networks trained on the BreaKHis dataset focus. 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