mlsanityData to 7 Oct 2026

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

arXiv 2205.08467 ↗First posted 17 May 2022eess.IV cs.CV cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 66 references that are in the map. First posted on arXiv 17 May 2022.

Builds on

14 of 66 references.

References · 14

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2021A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approachescited by 44
  5. 2020A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networkscited by 25
  6. 2021DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniquescited by 23
  7. 2021A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approachescited by 20
  8. 2021GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancercited by 17
  9. 2020Gastric histopathology image segmentation using a hierarchical conditional random fieldcited by 16
  10. 2021Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformerscited by 16
  11. 2020A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysiscited by 13
  12. 2020A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentationcited by 9
2 more
  1. 2021A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classificationcited by 9
  2. 2019Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Imagecited by 2

Built on by

Not cited yet.