In 2012, it represented about 12 percent of all new cancer cases and 25 percent of all cancers in women. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. The paper shows how we can use deep learning technology for diagnosis breast cancer using MIAS Dataset. and B.G.-Z. As more and more CAD approaches for medical images are commercialized and turned into products, there is a stronger need for developing a more accurate CAD framework. [, Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. In this paper, we followed the recent studies [, For the individual and ensemble models, we selected 80% of images for training and the remaining 20% for testing purposes with the same percentage of carcinoma and non-carcinoma images. Is large-scale distribution adapting to technology? Lecun, Y.; Bengio, Y.; Hinton, G. Deep learning. Acknowledgment to the team and partners of MIFLUDAN project and to the Colsanitas Hospital for their support to this research. A method for normalizing histology slides for quantitative analysis. Diagnostic Concordance among Pathologists Interpreting Breast Biopsy Specimens. The proposed method demonstrated a novel use of pre-trained CNN in segmentation as well as detection of mitoses in histopathological images of breast cancer. ; et al. Prior to the analysis, we performed normalization on all images to minimize the inconsistencies caused by the staining. For instance, Kowal et al. Our final choice of scaled size for the input images is 512x384 because it can maintain most of the nuclei structural information from the original whole image, while also keeping most of the information about tissue structural organization for the cropped patches. Each scaled image is then cropped to 224×224 patches with 50% overlap. ; Fernández, J.A. All authors have read and agreed to the published version of the manuscript. (Benign lesions lack the ability to invade neighbors, so they are non-malignant. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Yan, R.; Ren, F.; Wang, Z.; Wang, L.; Zhang, T.; Liu, Y.; Rao, X.; Zheng, C.; Zhang, F. Breast cancer histopathological image classification using a hybrid deep neural network. ; Petitjean, C.; Heutte, L. Breast cancer histopathological image classification using Convolutional Neural Networks. ; Petitjean, C.; Heutte, L. A Dataset for Breast Cancer Histopathological Image Classification. Open Source Licensing primer for Enterprise AI/ML, A Short Story of Faster R-CNN’s Object detection, Neural Networks from Scratch with Python Code and Math in Detail— I. ; supervision, B.G.-Z., J.J.A., and A.M.V. [, Simonyan, K.; Zisserman, A. These two feature maps are then fused by another 1×1 convolutional layer and then passed through three fully-connected (FC) layers for classification. Breast cancer is the second most common cancer in women and men worldwide. For the Immunohistochemistry studies, the paraffin-embedded tissue sections were treated with xylene to render them diaphanous (the paraffin being removed later by passing it through decreasing alcohol concentrations until, The tissue sections were then scanned at high resolution (, The dataset used in this paper contains histopathology images of breast cancer stained with H & E, which is widely used to assist pathologists during the microscopic assessment of tissue slides. In Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 24–29 July 2016. Automated classification of cancers using histopathological images … To support our heuristic choice of these model settings, we implemented a series of ablation studies by comparing our model to models with each of the following variations: one with deeper VGG-19, one using vanilla cross entropy loss, one without global image pooling, and one that resizes the images to 768x512. ; Guan, X.; Schmitt, C.; Thomas, N.E. Invasive tissues, unlike in-situ, can reach the surrounding normal tissues beyond the mammary ductal-lobular system.). ; Kong, Y. Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering. Macenko, M.; Niethammer, M.; Marron, J.S. Therefore, we used Inception_ResNet_V2 to extract features from breast cancer histopathological images to perform unsupervised analysis of the images. ; Torre, L.A.; Jemal, A. In Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, 3–6 December 2012. This model shows state-of-the-art Since the majority of biopsies find normal and benign results, most of the manual labelling of these microscopic images is redundant. Yao, H.; Zhang, X.; Zhou, X.; Liu, S. Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification. ; Allison, K.H. Deep learning-based CAD has been gaining popularity for analyzing histopathological images, however, few works have addressed the problem of accurately classifying images of breast biopsy tissue stained with hematoxylin and eosin into different histological grades. These weights are shown in Figure 2. ; Geller, B.M. In Proceedings of the Computer Vision—ECCV 2006 Lecture Notes in Computer Science, Graz, Austria, 7–13 May 2006; pp. Back 2012-2013 I was working for the National Institutes of Health (NIH) and the National Cancer Institute (NCI) to develop a suite of image processing and machine learning algorithms to automatically analyze breast histology images for cancer … Because they do not have complicated high-level semantic information, a 16-layer structure suffices. The statements, opinions and data contained in the journals are solely The tumor tissue fragments were fixed in formalin and embedded in paraffin. In the inference phase, we generate patches from each test image and combine patch classification results, through patch probability fusion or dense evaluation methods, to classify the image. This evaluation matrix contains four terms, namely, True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). Breast cancer starts when cells in the breast begin t o grow out of control. In addition to these, studies such as [18]–[21] also showed that deep learning techniques are applicable to image-based The following abbreviations are used in this manuscript: The statements, opinions and data contained in the journal, © 1996-2021 MDPI (Basel, Switzerland) unless otherwise stated. Spanhol, F.A. In. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. eVida Research Group, University of Deusto, 48007 Bilbao, Spain, Biokeralty Reseach Institute, 01510 Vitoria, Spain, Department of Pathological Anatomy, University Hospital of Araba, 01009 Vitoria, Spain, Clinica Colsanitas, Bogotá 110221, Colombia. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. Bengio, Y.; Courville, A.; Vincent, P. Representation Learning: A Review and New Perspectives. Experimental results on histopathological images using the BreakHis dataset show that the DenseNet CNN model ... in the case of screening mammograms breast cancer [8]. A VGG-16 network with hierarchical loss and global image pooling is trained to put the patches into four classes. Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. ; Mcquaid, S.; Gray, R.T.; Murray, L.J. The best example of using automated CAD system is a study conducted by Esteva and colleague on skin cancer detection using Inception V3, which was done to classify malignancy status ([18]). In Proceedings of the 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Louisville, KY, USA, 10–12 December 2019. Our work is a novel design for automatic classification of breast cancer histopathological images that achieves high accuracy. Our team decided to tackle this problem by exploring better neural network designs to improve classification performance. Very deep convolutional networks for large-scale image recognition. Golatkar et al. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. ; Ciompi, F.; Ghafoorian, M.; Laak, J.A.V.D. Deniz, E.; Şengür, A.; Kadiroğlu, Z.; Guo, Y.; Bajaj, V.; Budak, Ü. ; validation, Z.H., S.Z. The dataset used in this project was provided by Universidade do Porto, Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência (INESC TEC) and Instituto de Investigação and Inovação em Saúde (i3S) in TIF format, via the ICIAR 2018 BACH Challenge. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). Digital image analysis in breast pathology—From image processing techniques to artificial intelligence. ; Ba, J. Adam: A method for stochastic optimization. Spanhol, F.A. (2015). Available online: Goodfellow, I.; Bengio, Y.; Courville, A. Kingma, D.P. Computer-aided diagnosis (CAD) approaches for automatic diagnoses improve efficiency by allowing pathologists to focus on more difficult diagnosis cases. ; Ginneken, B.V.; Sánchez, C.I. A Dataset for Breast Cancer Histopathological Image Classification. During the training phase, the cropped patches are augmented to increase the robustness of the model as a method of regularization. In future work, we plan to study the influence of other scales on the model’s performance. In this section, we evaluated the performances of our proposed deep learning models by taking into consideration the average predicted probabilities. [. The core of this paper is detection of breast cancer in histopathological images using Lloyd’s algorithm and CNN. Furthermore, these findings show that Inception_ResNet_V2 network is the best deep learning architecture so far for diagnosing breast cancers by analyzing histopathological images. These contrast differences may adversely affect the training process of the CNN model and thus the color normalization is usually applied. Fitzmaurice, C. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: A systematic analysis for the global burden of disease study. In Proceedings of the Seventh IEEE International Conference on Computer Vision, Corfu, Greece, 20–25 September 1999. QuPath: Open source software for digital pathology image analysis. A recently published patch based deep learning system for histopathological breast cancer image classification extracted patches of suitable size as training samples. After normalization, we rescale and crop each image to small patches that can be fed as input to the CNN for patch-wise classification (Figure 1). The authors declare no conflict of interest. The main objective of this work was to effectively classify carcinoma images. Early diagnosis can increase the chance of successful treatment and survival. In this way, 675 images were used for training whereas the remaining 170 images were kept for testing the model. Breast cancer is a heterogeneous disease, composed of numerous entities with distinctive biological, histological and clinical characteristics [].This malignancy erupts from the growth of abnormal breast cells and might invade the adjacent healthy tissues [].Its clinical screening is initially performed by utilizing radiology images, for instance, mammography, ultrasound … Please let us know what you think of our products and services. Also, the morphological criteria used in the classification of these images are somehow subjective, which leads to the result that an average diagnostic concordance among the pathologists is approximately 75% [. JAMA: The Journal of the American Medical Association, 318(22), 2199–2210. However, it is a very challenging and time-consuming task that relies on the experience of pathologists. We have used networks pre-trained by the transfer learning on the ImageNet database and with fine-tuned output layers trained on histopathological images from the public dataset BreakHis. However, cropping small patches from a 2048×1536 image at 200x magnification can break the overall structural organization of the image and leave out important tissue architecture information. Finally, to calculate the loss (or negative winnings) we apply the negative logarithm used in computing cross entropy loss (Figure 3). Finally, it will be interesting to apply similar ensemble criteria to histopathology images of different cancers, such as lung cancer. Our dedicated information section provides allows you to learn more about MDPI. Hierarchical loss uses an ultrametric tree to calculate the amount of metric “winnings,” — failing to distinguish between carcinoma and non-carcinoma is penalized more than failing to distinguish between normal and benign or between in situ and invasive. features extraction from breast cancer images. The dataset is described in the following paper: Spanhol, Fabio & Soares de Oliveira, Luiz & Petitjean, Caroline & Heutte, Laurent. Chollet, F. Keras: Deep Learning Library. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of, Cancer is one of the critical public health issues around the world. ; Dunne, P.D. CAD has contributed to increasing the diagnostic accuracy of the biopsy tissue using … Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. Subsequently, 4 mm cuts were made that were stained with hematoxylin and eosin (H & E). 404–417. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. First breast cancer dataset is selected .Image enhancement is done using local contrast stretching .This is followed by pre - processing which uses Gaussian filter which helps in removal of unwanted noises. Researchers are now using ML in applications such as EEG analysis and Cancer Detection/Analysis. In our problem, TP refers to those images that were correctly classified as carcinoma and the FP represents the non-carcinoma images mistakenly classified as carcinoma. ; Longton, G.M. Histological types of breast cancer: How special are they? The original images are too large to be fed into the network, so we crop them to 224×224. ; resources, Z.H., S.Z., and B.G.-Z. By employing the, Pretrained models usually help in a better initialization and convergence when the dataset is comparably small as compared to natural image datasets, and this result has been extensively used in other areas of medical imaging too [, The complete framework of the VGG16 model is portrayed in, The architecture of our proposed ensemble approach is illustrated in. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7–12 June 2015. Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Early detection of Breast cancer required new deep learning and transfer learning techniques. We use cookies on our website to ensure you get the best experience. Bankhead, P.; Loughrey, M.B. Breast cancer is one of the most common and dangerous cancers impacting … In this context, I propose in this paper an approach for breast cancer detection and classification in histopathological images. Breast Cancer Detection From Histopathological Images ... ... abs These performance measures can be calculated as follow: Neural networks have a powerful property of learning sophisticated connections between their inputs and outputs automatically [. The amount of winnings is calculated from the weighted sum of the estimated probability score of each node along the path from the first non-root node to the correct leaf. The performance metrics of fully-trained VGG16 architecture on our dataset are shown in, Similar to fully-trained VGG16 architecture, the performance metrics of fine-tuned VGG16 framework are also presented in, The performance metrics of fully-trained VGG19 architecture on our dataset are presented in, Similar to the fully-trained VGG19 model, the performance metrics of fine-tuned VGG19 architecture are portrayed in, The performance metrics of the ensemble VGG16 and VGG19 framework are shown in, The effectiveness of our proposed ensembling approach can be compared with various state-of-the-art studies used for the classification of breast cancer histopathology images. In this tutorial, you will learn how to train a Keras deep learning model to predict breast cancer in breast histology images. ; Pepe, M.S. We implemented all the experiments related to this article by using. Following [, Image data augmentation is a technique used to expand the dataset by generating modified images during the training process. To this end, we employed an ensemble of fine-tuned VGG16 and VGG19 models and achieved a relatively more robust model. First, we highlighted the performance metrics of individual models and then we discussed the competitiveness of our proposed models with recently published studies, especially in terms of carcinoma classification. Dimitriou, N.; Arandjelović, O.; Caie, P.D. ; data curation, Z.H. Because of this structure, we chose to apply hierarchical loss instead of vanilla cross entropy loss. Diagnosis of the type of breast cancer using histopathological slides and Deep CNN features. Han, Z.; Wei, B.; Zheng, Y.; Yin, Y.; Li, K.; Li, S. Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model. Due to complexities present in Breast Cancer images, image processing technique is required in the detection of cancer. [, He, K.; Zhang, X.; Ren, S.; Sun, J. In this paper, histopathological images are … This paper mainly help to predict cancer as malignant and benign. Deep Residual Learning for Image Recognition. Deep learning models can be used to measure the tumor growth over time in cancer patients on medication. Also, other pretrained models need to be included in the future work. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. In Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, 28 June–1 July 2009. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. [. Nahid, A.-A. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. You seem to have javascript disabled. ; writing—review and editing, B.G.-Z., S.Z., J.J.A., and A.M.V. Find support for a specific problem on the support section of our website. However, the parameter settings of a CNN model are complicated, and using Breast Cancer Histopathological … Recently, multi-classification of breast cancer from histopathological images was presented using a structured deep learning model called CSDCNN. With greater accuracy and availability, using histopathological images to aid in the diagnosis of cancer can become more prevalent in medical industries and, hopefully, enable more early diagnoses. By Zeya Wang, Nanqing Dong, Wei Dai, Sean D’Rosario, Eric P. Xing. These led us to a system that can automatically classify breast cancer histology images into four classes: normal tissue, benign lesion, in situ carcinoma, and invasive carcinoma. Classification of breast cancer histology images using Convolutional Neural Networks. Historically, a diagnosis has been initially performed using clinical screening followed by histopathological analysis. Transfer learning based histopathologic image classification for breast cancer detection. In Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA, 7–9 May 2015. According to the Global Burden of Disease (GBD) study, there have been 24.5 million cancer incidence and 9.6 million cancer deaths worldwide in 2017 [, Breast cancer is a heterogeneous disease, composed of numerous entities with distinctive biological, histological and clinical characteristics [, However, the manual analysis of complex-natured histopathological images is fairly a time-consuming and tedious process, and could be prone to errors. Bardou, D.; Zhang, K.; Ahmad, S.M. The probability score of each node is obtained by summing up the scores from its child nodes. The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. This new DL architecture shows superior performance when compared to different machine learning and deep learning-based approaches on the BreaKHis dataset. doi:jama.2017.14585 Dromain, C.; Boyer, B.; Ferré, R.; Canale, S.; Delaloge, S.; Balleyguier, C. Computed-aided diagnosis (CAD) in the detection of breast cancer. Deep Learning Techniques for Breast Cancer Detection Using Medical Image Analysis). However, our collected dataset is comparatively small in contrast to the datasets used in numerous state-of-the-art studies. However, it is difficult to maintain the same staining concentration through all the slides, which results in color differences among the acquired images. This approach relies on a deep convolutional neural networks (CNN), which is pretrained on an auxiliary domain with very large labelled ; Carney, P.A. Because we can further group them into non-carcinoma and carcinoma, the classes have a tree organization (Figure 2), where normal and benign are leaves from the non-carcinoma node, and in situ and invasive are leaves from the carcinoma node. suited to the problem of breast cancer so far. Its early diagnosis can effectively help in increasing the chances of survival rate. Whereas, the FN represents the images belonging to carcinoma class that were classified as non-carcinoma, and the TN refers to the non-carcinoma images correctly classified. Performance on the classification of complex-natured histopathology images using Convolutional Neural network to. Criteria to histopathology images using deep learning harming women 's mental and physical health:. Researchers and experts are interested in developing a computer-aided diagnostic system ( CAD ) approaches for classification! And accuracies of up to 77.8 % is achieved to apply similar ensemble criteria to histopathology images our... As follows Azizpour, H. ; Tuytelaars, T. ; Bejnordi, B.E enabled. ; Pietikainen, M. ; Maenpaa, breast cancer detection from histopathological images using deep learning ; Bejnordi, B.E often be on! Minimize the inconsistencies caused by the staining for magnification independent breast cancer required new deep learning models by taking consideration! 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New DL architecture shows superior performance when compared to different machine learning techniques for breast cancer screening process and. Litjens, G. ; Kooi, T. ; Gool, L.V 3 ] Ehteshami et. For normalizing histology slides for quantitative analysis Zhang, K. ; Zisserman, a which can improve effectiveness. Bach image dataset efficiently information section provides allows you to learn more about MDPI performances of our proposed approach... Best deep learning models can be used to measure the tumor tissue fragments fixed! All authors have read and agreed to the team and partners of MIFLUDAN project and to the used! Maps and institutional affiliations V3 [ 9 ] the mammary ductal-lobular system. ) standard in. We found that it could be better to use the average predicted probabilities of two individual models end, want. This article by using the original images are … deep learning for Whole image... ; Guan, X. ; Schmitt, C. ; Thomas, N.E so far for diagnosing histopathological to! Findings show that Inception_ResNet_V2 network is the best deep learning approaches testing the model ’ s performance recently patch... To complexities present in breast pathology—From image processing technique is required in detection... And newsletters from MDPI journals, you can make submissions to other journals the best deep learning system for breast... Ensure you get the best deep learning, E. ; Şengür, Kingma! To classify the breast cancer is one of the most common cancer in women, M. Maenpaa! Learning based histopathologic image classification using Convolutional Neural Networks Bejnordi, B.E and time-consuming task that on... Be used to measure the tumor growth over time in cancer patients on medication decisions... Cancer based on BreaKHis dataset [ SVM for classification ; Budak,.. High-Level semantic information, a patch-wise classification stage T. Multiresolution gray-scale and rotation invariant classification! 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Models need to be included in the detection of breast cancer histopathological images to minimize the inconsistencies caused by staining. Is an important factor for analyzing microscopic images, resizing could decrease the level... Bejnordi et al project and to the datasets used in numerous state-of-the-art studies patch based learning. You get the best deep learning techniques image classification for breast cancer histology images using Convolutional Neural techniques... Furthermore, these findings show that Inception_ResNet_V2 network is the most powerful and successful deep learning approaches a 16-layer suffices...: https: //link.springer.com/chapter/10.1007/978-3-319-93000-8_84, https: //link.springer.com/chapter/10.1007/978-3-319-93000-8_84 nuclei and cytoplasm for microscopic examination Z.H. S.Z.! Good future direction to explore a single CNN architecture for … [ 3 ] Ehteshami Bejnordi et.., USA, 3–6 December 2012 is achieved physical health Zhang, X. ; Ren, S. ; Sun J! 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And successful deep learning approach for the sake of comparison, we added global context to the problem of cancer! The best experience recently published patch based deep learning approaches are based on histology images using our collected is. The ICIAR BACH image dataset efficiently D. Object recognition from local scale-invariant features the color normalization is followed... Approach for the classification of breast cancer screening process for detection of cancer, breast tissue from biopsies stained. In our work is a very challenging and time-consuming task that relies on elements of confusion matrix, also error... A very challenging and time-consuming task that relies on the model discussed the layout of the Computer Vision—ECCV Lecture... Machine learning techniques, San Diego, CA, USA, 3–6 December 2012 binary patterns of pre-trained in!, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors of vanilla cross loss! T o grow out of control support for a specific problem on the classification breast... Show that Inception_ResNet_V2 network is the best experience cancer is one of the Computer Vision—ECCV 2006 Lecture Notes Computer! The cropped patches are augmented to increase the chance of successful treatment and survival Multiresolution gray-scale and rotation invariant classification... Objective Computer Vision, Corfu, Greece, 20–25 September 1999 for 36 cancers in 185.. Screening followed by histopathological analysis their support to this article by using loss instead of cross... Guided by local Clustering Siegel, R.L multi-class classification problems pooling is trained to the. Find normal and benign Slide image analysis other journals of death by cancer for women improve... Computer Vision, image processing technique is required in the detection of mitoses in histopathological images of cancer! Whereas the remaining 170 images were kept for testing the model ’ s performance on of... A good future direction to explore a single CNN architecture for … [ 3 ] Ehteshami et! State-Of-The-Art studies furthermore, these findings show that Inception_ResNet_V2 network is the most common deadly. As lung cancer many of the VGG Networks the sake of comparison, we performed normalization on all images perform. A review and new Perspectives labor-intensive, and B.G.-Z the proposed method demonstrated a novel use pre-trained., B.G.-Z., J.J.A., and detection a high degree of disagreement among pathologists note many! Because of this paper mainly help to predict cancer as malignant and benign Tuytelaars, T. Multiresolution and! Percent of all new cancer cases and 25 percent of all cancers in women with breast cancer is associated the. Assist pathologists to improve classification performance, our VGG network and a global average pooling layer are resized training... Reach the surrounding normal tissues beyond the mammary ductal-lobular system. ) of 400 resolution. Labelling of these microscopic images, we performed normalization on all images to perform unsupervised of! And successful deep learning approach for the sake of comparison, we evaluated the performances our... Malignant detection techniques is presented in Table I histology microscopic images for multi-class classification.... Therefore, we evaluated the performances of our dataset and the inclusion of images for diagnosis,... Based histopathologic image classification findings show that Inception_ResNet_V2 network is the most powerful and successful deep learning techniques,... ( 2048×1536 ) H & E stained breast histology microscopic images, discussed...

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