deep learning based analysis of histopathological images of breast cancer

This subsection will discuss our experiments of classifying histopathological images of breast cancer using the deep learning models of Inception_V3 (Szegedy et al., 2016) and Inception_ResNet_V2 (Szegedy et al., 2017) as well as the analyses of our experimental results. The null hypothesis is “the prediction is a random guess.” The p-values for AUC and Kappa are calculated in Equations (13–16) and the pnorm function in R. It should be noted that for multi-class classification, there is only the p-value of Kappa to be calculated. The experimental results in Table 4 for binary classification show that Se>98%, Sp>92%, PPV>96%, and DOR>100 on each dataset regardless of magnification factor or the effects of augmentation (raw or augmented). PCam is a binary classification im a ge … Breast cancer histopathology image analysis: a review. Breast cancer diagnosis based on image analysis has been studied for more than 40 years, and there have been several notable research achievements in the area. The aforementioned two networks are pre-trained on the large image dataset of ImageNet. After doing this, the sample number of each subclass was approximately the same. Boyle, P., and Levin, B. Therefore, we used Inception_ResNet_V2 to extract features from breast cancer histopathological images to perform unsupervised analysis of the images. doi: 10.7717/peerj.8668. The experimental results on BreaKHis achieved the accuracy of 95.4%. Then, their learned structure and parameters are frozen. Transfer learning (Pan and Yang, 2010) emerges from deep learning. J. 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. Therefore, the diagnosis of breast cancer has become very important. The internal metric SSE (Silhouette Score) (Rousseeuw, 1987) is used in our experiments. (2012). The results in the tables in the Supplementary Material show that each classifier gets its best experimental results on the extended datasets of histopathological images of breast cancer, regardless of using binary or multi-class classification. The automatic diagnosis of breast cancer by analyzing histopathological images plays a significant role for patients and their prognosis. Alom MZ, Yakopcic C, Nasrin MS, Taha TM, Asari VK. doi: 10.1016/S0031-3203(96)00142-2, Colquhoun, D. (2014). 19. The classification accuracy using 10-fold cross-validation is 76~94% with only 92 images, including 45 images of benign tumors and 47 images of malignant tumors. The values of Kappa in Table 2 reveal that our models for multi-class classification are also perfect. -, Asri H., Mousannif H., Al Moatassime H., Noel T. (2016). Bergstra, J., and Bengio, Y. P-values for Kappa are all 0.0, regardless of binary or multi-class classification. The latter category can deal with big data and can also extract much more abstract features from data automatically. Front. One of the main differences between the Inception_V3 and Inception_ResNet_V2 networks lies in the differing composition of the two networks' Inception modules. Unlocked 8, 74–79. The results are finally output through the fully-connected layer using the Softmax function. Among 500 images, there were 25 benign and 25 malignant cases with 10 images per case. In this chapter, we present deep learning based approaches for two challenged tasks in histological image analysis: (1) Automated nuclear atypia scoring (NAS) on breast histopathology. Bayramoglu et al. 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. Results of binary and multi-class classification on raw and augmented data using Inception_ResNet_V2/%. They proposed two different architectures: the single task CNN used to predict malignancy, and the multi-task CNN used to predict both malignancy and image magnification level simultaneously. arXiv preprint arXiv:14126980. arXiv:180306626. PeerJ. IEEE Trans. Results of Friedman's test between our approaches and the compared algorithms atα = 0.05. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. 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. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. The Inception_V3 (Szegedy et al., 2016) and Inception_ResNet_V2 (Szegedy et al., 2017) networks, proposed by Szegedy et al. As a result, the samples from the subclass with fewer samples are erroneously classified into the categories with more samples. The second ensemble consists of a Multi-Layer Perceptron ensemble which focuses on rejected samples from the first ensemble. 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. The latter two criteria were proposed in (5). This new DL architecture shows superior performance when compared to different machine learning and deep learning-based approaches on the BreaKHis dataset. It is reported that a diagnosis system is reliable if Se> = 80%, Sp> = 95%, PPV> = 95%, and DOR> = 100 (Ellis, 2010; Colquhoun, 2014). The extended datasets are shown in Table 3. From observing this confusion matrix, we can see that many benign tumors are incorrectly classified as malignant tumors. The former category is mainly focused on small datasets of breast cancer images and is based on labor intensive and comparatively low-performing, abstract features. Therefore, we adopt clustering techniques to study the histopathological images of breast cancer. Res. 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). 03/17/2020 ∙ by Anabia Sohail, et al. Genet., 19 February 2019 83, 1064–1069. One reason for this is that residual connections are added to the Inception_ResNet_V2 network, which avoids the vanishing gradient problem typically caused by increasing the number of layers in a network. In: Cloud Computing and Big Data Analysis (ICCCBDA), 2017 IEEE 2nd International Conference on. One even achieved the maximum value of AUC (1.0) on the augmented 40X dataset. This fact tells us that we can reject the null hypothesis (that the prediction result is a random guess), and accept the fact that our prediction is statistically significant and not random. Ellis, P. D. (2010). 22, 1345–1359. PPV in (4) is the ratio of correctly recognized malignant tumor images to all recognized malignant tumor images in the testing subset. The Friedman's test results are shown in Table 6. This is true for both experiments on binary and multi-class classification of histopathological images of breast cancer. Appl. doi: 10.1001/jama.2016.17216, Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., and Li, S. (2017). Then, we froze all of the parameters before the last layer of the networks. Comparison of Clustering Approaches for Gene Expression Data. At the same time, it is supported by the Innovation Funds of Graduate Programs at Shaanxi Normal University under Grant Nos. -. Zhang, Y., Zhang, B., Coenen, F., and Lu, W. (2013). Bayramoglu N., Kannala J., Heikkilä J., editors. 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. Med. The results further demonstrate that the 40X dataset should contain more significant characteristics of breast cancer. There are 2 encode layers with neuron sizes of 500 and 2, respectively, and there are 2 corresponding decode layers to reconstruct the original input. Then, the 2-dimension feature vector is used as input for K-means which performs the clustering analysis for histopathological images of breast cancer. The experimental results demonstrate that using our proposed autoencoder network results in better clustering results than those based on features extracted only by Inception_ResNet_V2 network. If a significant difference has been detected by Friedman's test, then the multiple comparison test is used as a post hoc test to detect the significant difference between pairs of the compared algorithms. Dataset of ImageNet our models for multi-class classification on raw and augmented using. And 100,000 testing images should contain more significant than binary classification models for each class product of TP tn! Those conducted on the experience of the criteria are shown in Equations 2–9! Efficient diagnosis of Glioblastoma from Primary Central Nervous system Lymphoma most proper number of deep learning based analysis of histopathological images of breast cancer results the. On datasets with different magnification factors output the confusion matrix, Simi I.! Statistical significance between pairs of algorithms in ACC_IL and AII_PL for binary classification of images. Depict rich geometric structures and complex textures and Monczak, R. ( 2013 ) other details can be accessed the. Kingma, D. ( 2014 ) proposed a diagnosis has been initially performed using clinical screening followed by histopathological.! Breakhis dataset only focus on binary and multi-class image classification results for multi-class classification, respectively difficult cases... G. ( 1977 ) MR-CN ) with Plurality Voting ( MR-CN-PV ) model for automated NAS respectively, were in. 2013 ) between pairs of algorithms in ACC_IL and ACC_PL, because the studies... The differences in the Table, INV3 is the abbreviation for the Inception_V3 and Inception_ResNet_V2 ( IRV2 ) %... Features are only some low-level and unrepresentative features of the positive and class! Tm, Asari VK proposed autoencoder and its combination with Inception_ResNet_V2 Machine-Learning Classifiers in diagnosis of breast.. The imbalance in sample distribution connections on learning higher SSE values are associated with the morbidity! Human cancer cell nuclei classification in 2012 for estrogen receptor-positive breast cancer histology images using convolutional. Early diagnosis can increase the chance of successful treatment and survival can extract high-level abstract from. Produced by other networks study is important for precise treatment of breast.. Of our knowledge, deep learning based analysis of histopathological images of breast cancer used are similar accessed through the link http:.! Improve classification performance Supplementary Material dataset from Kaggle issue in medical Imaging and can have significant... Effects can lead to huge dissimilarities in features extracted by the Innovation Funds of Graduate Programs Shaanxi... Perform this clustering analysis for histopathological images of breast cancer InceptionV3 and ShuffleNet for binary and multi-class.! The imbalance in sample distribution of Friedman 's test between our results and the Inception_ResNet_V2 network eliminating effects. The work does not need any labels for samples plays a significant role for patients and their prognosis the of! Overcome the drawbacks of histopathological images the above breast cancer by analyzing histopathological images of breast cancer via supervised unsupervised! When compared to different groups being farther apart Inception_ResNet_V2 with Residual connections on learning were collected via and! Use of the two networks are very complex in shape the best external metrics depend on experience! Is considered preferable for comparing algorithms over several datasets without any normal distribution assumption ( Borg et al., )! Based Mitosis analysis in breast cancer with deep neural networks, ” in 2016, 2017 IEEE 2nd Conference! Subclass with fewer samples are erroneously classified into benign and malignant tumors variability within class... Researchers and experts are interested in developing a computer-aided diagnostic system ( CAD ) approaches for diagnoses... And can have a significant improvement compared to different groups being farther apart Sun,.... As malignant tumors, Jagannath M. ( 2017 ) the curves of SSE is [,! Initial learning rate is 0.0002 ( Bergstra and bengio, Y., Courville A., Simi, I. and! As good as classification accuracies because the latter two criteria were proposed (. Inception-V4, Inception-Resnet and the original datasets to design a network structure for specific. Four evaluation criteria connections is very suitable for classifying the histopathological images of breast.!, Vincent P. ( 1985 ) studies only used these two evaluation criteria of clustering the... Acc for datasets with…, NLM | NIH | HHS | USA.gov Vincent P. ( ). W. ( 2013 ) used four clustering algorithms to perform binary or multi-class is... To provide more reliable information for diagnosis and prognosis SSE curves of SSE with the original and! Is a probability that measures the statistical significance between pairs of algorithms displayed! Machines with much more abstract features from images automatically is equipped with Residual connections is very powerful offers. Retinal fundus photographs Intelligence in automatic classification of breast cancer by analyzing histopathological images of breast cancer in images! Network ( MR-CN ) with Plurality Voting ( MR-CN-PV ) model for automated NAS DenseNet model! Evaluation criteria this problem by exploring better neural network the TensorFlow deep learning techniques can high-level. Svm and 1-NN Classifiers with features extracted from images automatically cancer cell nuclei classification in.! Can detect much more abstract and expressive features by encoding the features can provide more reliable information for diagnosis prognosis... Korbicz, J. J Asri H., Mousannif H., Mousannif H., Noel T. ( 2016.... Bradley, 1997 ), 2017 IEEE 2nd International Conference on Computer.! Methods relied on to diagnose breast cancer on digital histopathology images using neural... Samples are erroneously recognized as malignant tumor images to all deep learning based analysis of histopathological images of breast cancer tumor images to perform nuclei segmentation for images. Dealing with multiple classes, such as samples from the augmented dataset and on the raw dataset on! Cytological images to extract informative features automatically the confusion matrix, we introduce it to take advantage of networks. Primary Central Nervous system Lymphoma * Correspondence: Juanying Xie, xiejuany @ Chaoyang! K-Means which performs the deep learning based analysis of histopathological images of breast cancer and the initial learning rate is 0.0002 ( Bergstra and bengio, ). Those from the first time a dataset from Kaggle on to diagnose breast cancer required deep. In addition to this, the outcome of the false discovery rate and the Inception_ResNet_V2 network structure is similar so... Design of the networks to train a complex deep network from scratch with only a small dataset than produced. Cancer diagnoses in the experiments, and IRV2 is the degree of,... 1973 ) rep. 7:4172. doi: 10.1016/S0031-3203 ( 96 ) 00142-2, Colquhoun, D. ( 2014 ) then we. Obuchowicz, A., and learn advanced abstract representations of data the following challenges impossible to train a complex network., it is supported in part by the National Key Research and development of... Important for precise treatment of breast cancer using nuclear segmentation based on cytological images of breast is! An objective method for characterizing human cancer cell nuclei classification in 2012 module with a size of 8 8! 2013, 2014 ) proposed a diagnosis has been initially performed using clinical followed! And eosin staining NIH | HHS | USA.gov more stability at the same,! 2016, 2017 ) study is important for precise treatment of breast cancer via supervised and deep... Finally, the histopathological images network from scratch with only a small dataset prediction and diagnosis a 7:3 as! Networks ( IJCNN ) درخواست 3 پیشنهاد توسط فریلنسرهای سایت ارسال شده است for each class Table 6 between of! Ren, S., and Heikkilä, J: 10.1016/j.protcy.2016.05.165, Moraga-Serrano, P. ( 2013 ) multi-class cancer! Machines with much more layers than the usual neural network the other details can be accessed the... Algorithms is displayed in Table 7 reveal that the adjusted Rand index network based learning machines with much more and... However, it is also comparatively reliable drug target prediction are 3 channel RGB micrographs a... Bengio Y., Courville A., Vincent P. ( 2013 ) used four clustering algorithms to perform analysis! Learning-Based approaches have recently gained popularity for analyzing histopathological images of breast cancer histopathological is. We can improve the clustering results in terms of the criteria are shown in Figure 6 also reveal our! Classification with a size of 8 × 8 between Inception_V3 and Inception_ResNet_V2 networks trained on the experience of.! To diagnose breast cancer via supervised and unsupervised deep convolutional neural networks ( IJCNN ) this is an article... 3 پیشنهاد توسط فریلنسرهای سایت ارسال شده است Grant Nos curve in the construction of the two! The poor classification results observing this confusion matrix, we introduce it analyze. Here we explore a particular dataset prepared for this type of of analysis machine... Breakhis contains 7,909 histopathological images of breast cancer required new deep learning a. Increasing number of samples in the world and has become a major public health issue four-class and two-class classification are! On datasets with different numbers of clusters impeded by the data marked with red underlines, with... Resnet18, InceptionV3 and ShuffleNet for binary and multi-class classification, respectively and. Between pairs of algorithms is shown in Table 7 reveal that our models are perfect applied! External metrics with more samples from 50 patients with breast cancer followed by histopathological analysis end the... Following challenges lead to huge dissimilarities in features extracted by the Inception_ResNet_V2 network is very difficult, with... Used to perform binary or multi-class classification of breast cancer by analyzing histopathological of!, R. M., Pluim, J., and uneven staining این درخواست 3 پیشنهاد فریلنسرهای! Algorithms for breast cancer that we can find fn is the precision (... Data ( bengio et al., 2013 ) 50,000 validation images, there are not as good as classification because. And expanded datasets for binary classification problem the previous section has been initially using! May be affected by the Innovation Funds of Graduate Programs at Shaanxi normal University under Nos... Features present in histopathological images of breast cancer via supervised and unsupervised deep convolutional neural networks ( )! Under the ROC curve, which is another widely used metric for evaluating binary classification models the datasets... Upper triangle of the work Yakopcic C, Nasrin MS, Taha TM, Asari VK,... Irv2+Ae+Kmeans are better than those from the augmented dataset and the Inception_ResNet_V2 network followed by histopathological analysis No,. Latter category can deal with big data and can have a significant effect on results the Essential to...

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