Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP) represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT) gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT) derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a) Convolutional Neural Network Raw Image (CNN-I); (b) Convolutional Neural Network CT Histogram (CNN-CH); (c) Convolutional Neural Network CT LBP (CNN-CL); (d) Convolutional Neural Network Discrete Fourier Transform (CNN-DF); (e) Convolutional Neural Network Discrete Cosine Transform (CNN-DC). We have performed our experiments on the BreakHis image dataset. The best performance is achieved when we utilize the CNN-CH model on a 200× dataset that provides Accuracy, Sensitivity, False Positive Rate, False Negative Rate, Recall Value, Precision and F-measure of 92.19%, 94.94%, 5.07%, 1.70%, 98.20%, 98.00% and 98.00%, respectively.

[1]  Hadi Rezaeilouyeh,et al.  Microscopic medical image classification framework via deep learning and shearlet transform , 2016, Journal of medical imaging.

[2]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[3]  Gilbert Strang,et al.  The Discrete Cosine Transform , 1999, SIAM Rev..

[4]  Luiz Eduardo Soares de Oliveira,et al.  A Dataset for Breast Cancer Histopathological Image Classification , 2016, IEEE Transactions on Biomedical Engineering.

[5]  Esmat Rashedi,et al.  Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm , 2016, 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC).

[6]  Yaoqin Xie,et al.  Breast mass lesion classification in mammograms by transfer learning , 2017, BIOINFORMATICS 2017.

[7]  Hamidreza Rashidy Kanan,et al.  Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution , 2015, Comput. Methods Programs Biomed..

[8]  Xinbo Gao,et al.  A deep feature based framework for breast masses classification , 2016, Neurocomputing.

[9]  Estela López-Olazagasti,et al.  BICAD: Breast image computer aided diagnosis for standard BIRADS 1 and 2 in calcifications , 2012, CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers.

[10]  Nikolas P. Galatsanos,et al.  A support vector machine approach for detection of microcalcifications , 2002, IEEE Transactions on Medical Imaging.

[11]  Smriti H. Bhandari,et al.  Improved approach towards classification of histopathology images using bag-of-features , 2016, 2016 International Conference on Signal and Information Processing (IConSIP).

[12]  George Hamer,et al.  Enhanced Breast Cancer Classification with Automatic Thresholding Using SVM and Harris Corner Detection , 2016, RACS.

[13]  A Bazzani,et al.  An SVM classifier to separate false signals from microcalcifications in digital mammograms , 2001, Physics in medicine and biology.

[14]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[15]  Ketan Sharma,et al.  Classification of mammogram images by using CNN classifier , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[16]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[17]  Zohreh Azimifar,et al.  Contourlet-Based Mammography Mass Classification , 2007, ICIAR.

[18]  Akira Hasegawa,et al.  Classification of microcalcifications in radiographs of pathological specimen for the diagnosis of breast cancer , 1994, Medical Imaging.

[19]  Tao Jiang,et al.  Evaluating Diagnostic Performance of Machine Learning Algorithms on Breast Cancer , 2015, IScIDE.

[20]  Nico Karssemeijer,et al.  Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..

[21]  Mengjie Zhang,et al.  Evolutionary algorithms for classification of mammographie densities using local binary patterns and statistical features , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[22]  Anne L. Martel,et al.  Classification of Dynamic Contrast-Enhanced Magnetic Resonance Breast Lesions by Support Vector Machines , 2008, IEEE Transactions on Medical Imaging.

[23]  Yongming Li,et al.  Automatic cell nuclei segmentation and classification of breast cancer histopathology images , 2016, Signal Process..

[24]  Marek Kowal,et al.  Classification of breast cancer cytological specimen using convolutional neural network , 2017 .

[25]  Miguel Ángel Guevara-López,et al.  Representation learning for mammography mass lesion classification with convolutional neural networks , 2016, Comput. Methods Programs Biomed..

[26]  A. Govardhan,et al.  Nonsubsampled Contourlet Transform Based Classification of Microcalcification in Digital Mammograms , 2012 .

[27]  Yuanjie Zheng,et al.  Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model , 2017, Scientific Reports.

[28]  K. Thangavel,et al.  Mammogram Image Classification Using Rough Neural Network , 2014 .

[29]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Bailing Zhang,et al.  Breast cancer diagnosis from biopsy images by serial fusion of Random Subspace ensembles , 2011, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI).

[31]  René V. Mayorga,et al.  An automated confirmatory system for analysis of mammograms , 2016, Comput. Methods Programs Biomed..

[32]  Bailing Zhang,et al.  Breast Cancer Classification From Histological Images with Multiple Features and Random Subspace Classifier Ensemble , 2011 .

[33]  Samuel Cheng,et al.  An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology , 2016, SPIE Medical Imaging.

[34]  S. Anand,et al.  Detection of architectural distortion in mammogram images using contourlet transform , 2013, 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN).

[35]  Wagner Coelho A. Pereira,et al.  Breast tumor classification in ultrasound images using neural networks with improved generalization methods , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[36]  Makoto Yoshizawa,et al.  Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis , 2016, 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).

[37]  Nikos Grammalidis,et al.  Grading of invasive breast carcinoma through Grassmannian VLAD encoding , 2017, PloS one.

[38]  Edward J. Kendall,et al.  Automated Breast Image Classification Using Features from Its Discrete Cosine Transform , 2014, PloS one.

[39]  Ron Kimmel,et al.  Breast Cancer Diagnosis From Biopsy Images Using Generic Features and SVMs , 2006 .

[40]  Maurício Marengoni,et al.  Applying Artificial Neural Network for the Classification of Breast Cancer Using Infrared Thermographic Images , 2016, ICCVG.

[41]  Goreti Marreiros,et al.  Using Data Mining Techniques to Support Breast Cancer Diagnosis , 2015, WorldCIST.

[42]  Nadira Banu Kamal,et al.  MRI mammogram image classification using ID3 algorithm , 2012 .

[43]  Lior Rokach,et al.  Using the confusion matrix for improving ensemble classifiers , 2010, 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel.

[44]  Luiz Eduardo Soares de Oliveira,et al.  Breast cancer histopathological image classification using Convolutional Neural Networks , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[45]  Angel Cruz-Roa,et al.  Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features , 2014, Journal of medical imaging.

[46]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[47]  J. Dheeba,et al.  Classification of malignant and benign microcalcification using SVM classifier , 2011, 2011 International Conference on Emerging Trends in Electrical and Computer Technology.