Histopathological Breast-Image Classification Using Concatenated R–G–B Histogram Information

Breast Cancer is a serious threat to women. The identification of breast cancer relies heavily on histopathological image analysis. Among the different breast-cancer image analysis techniques, classifying the images into Benign and Malignant classes, have been an active area of research. The involvement of machine learning for breast-cancer image classification is also an active area of research. Considering the importance of the breast-cancer image classification, this paper has classified a set of histopathological images into Benign and Malignant classes utilizing Neural Network techniques and Random Forest algorithms. As histopathological images suffer intensity variation, in this paper, we have normalized the intensity information by newly proposed intensity normalization techniques, and classify the images using Neural Network techniques and Tree-based classification tools. Investigation shows that the proposed Normalization technique gives the best performance when we use Neural Network techniques but Tree-based algorithms such as the Random Forest algorithm give better performance when we use images without normalization techniques.

[1]  Farida Cheriet,et al.  Multimodal image registration of the scoliotic torso for surgical planning , 2013, BMC Medical Imaging.

[2]  G. Annapoorani,et al.  Neural Network based classification for orthopedic conditions diagnosis using grey level co-occurrence probabilities , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[3]  Ming-Syan Chen,et al.  On Generalizable Low False-Positive Learning Using Asymmetric Support Vector Machines , 2013, IEEE Transactions on Knowledge and Data Engineering.

[4]  Takumi Kobayashi,et al.  Dirichlet-Based Histogram Feature Transform for Image Classification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Ahmed Bouridane,et al.  Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery , 2016, PloS one.

[6]  C. S. Rai,et al.  Artificial Neural Networks for developing localization framework in Wireless Sensor Networks , 2014, 2014 International Conference on Data Mining and Intelligent Computing (ICDMIC).

[7]  Guoyin Li,et al.  A modified Polak-Ribière-Polyak conjugate gradient algorithm for nonsmooth convex programs , 2014, J. Comput. Appl. Math..

[8]  Tore Nielsen,et al.  Revisiting the ROC curve for diagnostic applications with an unbalanced class distribution , 2013, 2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA).

[9]  Lilla Böröczky,et al.  Feature subset selection for improving the performance of false positive reduction in lung nodule CAD , 2006, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[10]  Vishal Monga,et al.  Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning , 2015, IEEE Transactions on Medical Imaging.

[11]  Roman Monczak,et al.  Computer-Aided Breast Cancer Diagnosis Based on the Analysis of Cytological Images of Fine Needle Biopsies , 2013, IEEE Transactions on Medical Imaging.

[12]  Po-Whei Huang,et al.  Effective segmentation and classification for HCC biopsy images , 2010, Pattern Recognit..

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

[14]  Hala H. Zayed,et al.  Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images , 2014, IEEE Systems Journal.

[15]  M. Duraisamy,et al.  cellular neural network based medical image segmentation using artificial bee colony algorithm , 2014, 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE).

[16]  Jae Won Lee,et al.  Content-based image classification using a neural network , 2004, Pattern Recognit. Lett..

[17]  S. Bacha,et al.  Appliance usage prediction using a time series based classification approach , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[18]  Thomas Filleron,et al.  Are Early Relapses in Advanced-Stage Ovarian Cancer Doomed to a Poor Prognosis? , 2016, PloS one.

[19]  Brian Borchers,et al.  Review of practical optimization: algorithms and engineering applications by Andreas Antoniou and Wu-Sheng Lu (Springer Verlag, 2007) , 2009, SIGA.

[20]  Amitava Chatterjee A Fletcher–Reeves Conjugate Gradient Neural-Network-Based Localization Algorithm for Wireless Sensor Networks , 2010, IEEE Transactions on Vehicular Technology.

[21]  Marek Kowal,et al.  Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images , 2013, Comput. Biol. Medicine.

[22]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[23]  S. Hewitt,et al.  Infrared spectroscopic imaging for histopathologic recognition , 2005, Nature Biotechnology.

[24]  Yinan Kong,et al.  Real-time edge detection and range finding using FPGAs , 2015 .

[25]  R. Kumar,et al.  Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features , 2015, Journal of medical engineering.

[26]  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.

[27]  May D. Wang,et al.  Histological image classification using biologically interpretable shape-based features , 2013, BMC Medical Imaging.