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.
Convolutional neural network Breast cancer K-means Autoencoder Support vector machine
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Li Y, Wu J, Wu Q (2019) Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access 7:21400–21408CrossRefGoogle Scholar
Vo DM, Nguyen N-Q, Lee S-W (2019) Classification of breast cancer histology images using incremental boosting convolution networks. Inf Sci 482:123–138CrossRefGoogle Scholar
Wahab N, Khan A, Lee YS (2017) Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection. Comput Bio Med 85:86–97CrossRefGoogle Scholar
Xie J, Liu R, Luttrell J, Zhang C (2019) Deep learning based analysis of histopathological images of breast cancer. Front Genet 10:80CrossRefGoogle Scholar
Mambou SJ, Maresova P, Krejcar O, Selamat A, Kuca K (2018) breast cancer detection using infrared thermal imaging and a deep learning model, (in eng). Sensors (Basel, Switzerland) 18(9):2799CrossRefGoogle Scholar
Chougrad H, Zouaki H, Alheyane O (2018) Deep convolutional neural networks for breast cancer screening. Comput Methods Programs Biomed 157:19–30CrossRefGoogle Scholar
Nahid A-A, Mehrabi AM, Kong Y (2018) Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. Biomed Res Int 03:07Google Scholar
Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International joint Conference on neural networks (IJCNN), pp 2560–2567Google Scholar
Roy K, Banik D, Bhattacharjee D, Nasipuri M (2019) Patch-based system for classification of breast histology images using deep learning. Comput Med Imaging Graph 71:90–103CrossRefGoogle Scholar
Gandomkar Z, Brennan PC, Mello-Thoms C (2018) MuDeRN: multi-category classification of breast histopathological image using deep residual networks. Artif Intell Med 88:14–24CrossRefGoogle Scholar
Zheng Y et al (2017) Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification. Pattern Recogn 71:14–25CrossRefGoogle Scholar
Jiao Z, Gao X, Wang Y, Li J (2016) A deep feature based framework for breast masses classification. Neurocomputing 197:221–231CrossRefGoogle Scholar
Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S (2017) Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep 7(1):4172CrossRefGoogle Scholar
Sudharshan PJ, Petitjean C, Spanhol F, Oliveira LE, Heutte L, Honeine P (2019) Multiple instance learning for histopathological breast cancer image classification. Exp Syst Appl 117:103–111CrossRefGoogle Scholar
Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R (2018) Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 47:45–67CrossRefGoogle Scholar
Araújo T et al (2017) Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12(6):e0177544MathSciNetCrossRefGoogle Scholar
Suzuki S, et al (2016) Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis. In: 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp 1382–1386Google Scholar
Ragab DA, Sharkas M, Marshall S, Ren J (2019) Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ 7:e6201CrossRefGoogle Scholar
Mohamed AA, Berg WA, Peng H, Luo Y, Jankowitz RC, Wu S (2018) A deep learning method for classifying mammographic breast density categories. Med Phys 45(1):314–321CrossRefGoogle Scholar
Al-masni MA et al (2018) Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed 157:85–94CrossRefGoogle Scholar
Asri H, Mousannif H, Moatassime HA, Noel T (2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci 83:1064–1069CrossRefGoogle Scholar