Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer

The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. In this paper, based on the study of computer aided diagnosis system, graph based features are applied to gastric cancer histopathology microscopic image analysis, and a classifier is used to classify gastric cancer cells from benign cells. Firstly, image segmentation is performed, and after finding the region, cell nuclei are extracted using the k-means method, the minimum spanning tree (MST) is drawn, and graph based features of the MST are extracted. The graph based features are then put into the classifier for classification. In this study, different segmentation methods are compared in the tissue segmentation stage, among which are Level-Set, Otsu thresholding, watershed, SegNet, U-Net and Trans-U-Net segmentation; Graph based features, Red, Green, Blue features, Grey-Level Co-occurrence Matrix features, Histograms of Oriented Gradient features and Local Binary Patterns features are compared in the feature extraction stage; Radial Basis Function (RBF) Support Vector Machine (SVM), Linear SVM, Artificial Neural Network, Random Forests, k-NearestNeighbor, VGG16, and Inception-V3 are compared in the classifier stage. It is found that using U-Net to segment tissue areas, then extracting graph based features, and finally using RBF SVM classifier gives the optimal results with 94.29%.

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.