Histopathological Image Classification With Color Pattern Random Binary Hashing-Based PCANet and Matrix-Form Classifier

The computer-aided diagnosis for histopathological images has attracted considerable attention. Principal component analysis network (PCANet) is a novel deep learning algorithm for feature learning with the simple network architecture and parameters. In this study, a color pattern random binary hashing-based PCANet (C-RBH-PCANet) algorithm is proposed to learn an effective feature representation from color histopathological images. The color norm pattern and angular pattern are extracted from the principal component images of R, G, and B color channels after cascaded PCA networks. The random binary encoding is then performed on both color norm pattern images and angular pattern images to generate multiple binary images. Moreover, we rearrange the pooled local histogram features by spatial pyramid pooling to a matrix-form for reducing the dimension of feature and preserving spatial information. Therefore, a C-RBH-PCANet and matrix-form classifier-based feature learning and classification framework is proposed for diagnosis of color histopathological images. The experimental results on three color histopathological image datasets show that the proposed C-RBH-PCANet algorithm is superior to the original PCANet and other conventional unsupervised deep learning algorithms, while the best performance is achieved by the proposed feature learning and classification framework that combines C-RBH-PCANet and matrix-form classifier.

Authors

• Jun Shi School of Software, Hefei University of Technology
• Jinjie Wu
• Yan Li
• Qi Zhang School of Computer Science, Fudan University, Shanghai, China Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
• Shihui Ying

Keywords

• Image color analysis
• Principal component analysis
• Feature extraction
• Classification algorithms
• Histograms
• Algorithm design and analysis
• Image classification
• Color Pattern
• Histopathological Images
• Principal Component Analysis Network
• Histopathological Image Classification
• Histogram
• Learning Algorithms
• Spatial Information
• Local Features
• Effects Of Characteristics
• Feature Representation
• Feature Classification
• Image Dataset
• Learning Framework
• Feature Learning
• Feature Dimension
• Binary Image
• Color Channels
• Classification Framework
• Computer-aided Diagnosis
• Results Of Different Algorithms
• Binary Code
• Local Feature Extraction
• Deep Belief Network
• Binary Pattern
• Stacked Autoencoder
• Multiple Kernel Learning
• Support Vector Machine
• Color Information
• Classification Accuracy
• Color angular pattern
• color histopathological image
• color norm pattern
• matrix-form classifier
• PCANet
• random binary hashing
• Algorithms
• Breast Neoplasms
• Databases, Factual
• Female
• Histocytochemistry
• Humans
• Image Interpretation, Computer-Assisted
• Kidney Diseases
• Machine Learning
• ROC Curve