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

• URL: http://arxiv.org/abs/2205.08467v1
• Date: Tue, 17 May 2022 16:16:29 GMT
• Title: Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer
• Authors: Haiqing Zhang, Chen Li, Shiliang Ai, Haoyuan Chen, Yuchao Zheng, Yixin Li, Xiaoyan Li, Hongzan Sun, Xinyu Huang, Marcin Grzegorzek
• Abstract summary: Graph based features are applied to gastric cancer histopathology microscopic image analysis. 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%.
• Score: 7.607669435880715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%.

Related papers

• GRU-Net: Gaussian Attention Aided Dense Skip Connection Based MultiResUNet for Breast Histopathology Image Segmentation [24.85210810502592]
  This paper presents a modified version of MultiResU-Net for histopathology image segmentation. It is selected as the backbone for its ability to analyze and segment complex features at multiple scales. We validate our approach on two diverse breast cancer histopathology image datasets.
  arXiv  Detail & Related papers  (2024-06-12T19:17:17Z)
• Explainable and Position-Aware Learning in Digital Pathology [0.0]
  In this work, classification of cancer from WSIs is performed with positional embedding and graph attention. A comparison of the proposed method with leading approaches in cancer diagnosis and grading verify improved performance. The identification of cancerous regions in WSIs is another critical task in cancer diagnosis.
  arXiv  Detail & Related papers  (2023-06-14T01:53:17Z)
• Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning [0.0]
  This study aims to highlight the regions of interest used by a convolutional neural network (CNN) for classifying histological images as benign or malignant. We employed the VGG19 architecture and tested three visualization methods: Gradient, LRP Z, and LRP Epsilon. Based on the results obtained, the Gradient visualization method and the MeanShift selection method yielded satisfactory outcomes for visualizing the images.
  arXiv  Detail & Related papers  (2023-05-31T17:33:28Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Learning Hierarchical Graph Representation for Image Manipulation Detection [50.04902159383709]
  The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural Networks (CNNs) to capture the tampering artifacts left in the images. We propose a hierarchical Graph Convolutional Network (HGCN-Net), which consists of two parallel branches.
  arXiv  Detail & Related papers  (2022-01-15T01:54:25Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Features based Mammogram Image Classification using Weighted Feature Support Vector Machine [0.0]
  This paper considers automated classification of breast tissue type as benign or malignant using Weighted Feature Support Vector Machine (WFSVM) This analysis shows that the texture features have resulted in better accuracy than the other features with WFSVM and SVM.
  arXiv  Detail & Related papers  (2020-09-19T21:28:31Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net [60.145440290349796]
  The final diagnosis of prostate cancer is based on the visual detection of Gleason patterns in prostate biopsy by pathologists. Computer-aided-diagnosis systems allow to delineate and classify the cancerous patterns in the tissue. The methodological core of this work is a U-Net convolutional neural network for image segmentation modified with residual blocks able to segment cancerous tissue.
  arXiv  Detail & Related papers  (2020-05-22T19:49:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.