A novel approach for glaucoma classification by wavelet neural networks using graph-based, statisitcal features of qualitatively improved images

• URL: http://arxiv.org/abs/2206.12099v1
• Date: Fri, 24 Jun 2022 06:19:30 GMT
• Title: A novel approach for glaucoma classification by wavelet neural networks using graph-based, statisitcal features of qualitatively improved images
• Authors: N. Krishna Santosh, Dr. Soubhagya Sankar Barpanda
• Abstract summary: We have proposed a new glaucoma classification approach that employs a wavelet neural network (WNN) on optimally enhanced retinal images features. The performance of the WNN classifier is compared with multilayer perceptron neural networks with various datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we have proposed a new glaucoma classification approach that employs a wavelet neural network (WNN) on optimally enhanced retinal images features. To avoid tedious and error prone manual analysis of retinal images by ophthalmologists, computer aided diagnosis (CAD) substantially aids in robust diagnosis. Our objective is to introduce a CAD system with a fresh approach. Retinal image quality improvement is attempted in two phases. The retinal image preprocessing phase improves the brightness and contrast of the image through quantile based histogram modification. It is followed by the image enhancement phase, which involves multi scale morphological operations using image specific dynamic structuring elements for the retinal structure enrichment. Graph based retinal image features in terms of Local Graph Structures (LGS) and Graph Shortest Path (GSP) statistics are extracted from various directions along with the statistical features from the enhanced retinal dataset. WNN is employed to classify glaucoma retinal images with a suitable wavelet activation function. The performance of the WNN classifier is compared with multilayer perceptron neural networks with various datasets. The results show our approach is superior to the existing approaches.

Related papers

• Mew: Multiplexed Immunofluorescence Image Analysis through an Efficient Multiplex Network [84.88767228835928]
  We introduce Mew, a novel framework designed to efficiently process mIF images through the lens of multiplex network. Mew innovatively constructs a multiplex network comprising two distinct layers: a Voronoi network for geometric information and a Cell-type network for capturing cell-wise homogeneity. This framework equips a scalable and efficient Graph Neural Network (GNN), capable of processing the entire graph during training.
  arXiv  Detail & Related papers  (2024-07-25T08:22:30Z)
• TaGAT: Topology-Aware Graph Attention Network For Multi-modal Retinal Image Fusion [11.321411104729002]
  We propose the Topology-Aware Graph Attention Network (TaGAT) for multi-modal retinal image fusion. Our model outperforms state-of-the-art methods in Fluorescein Fundus Angiography (FFA) with Color Fundus (CF) and Optical Coherence Tomography ( OCT) with confocal microscopy retinal image fusion.
  arXiv  Detail & Related papers  (2024-07-19T10:32:06Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification [28.949527817202984]
  We propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network. We build a 14-layer deep graph convolutional network to handle cell graph data. We evaluate our approach on the private LUAD7C and public colorectal cancer datasets.
  arXiv  Detail & Related papers  (2023-08-24T12:27:03Z)
• Bridging Synthetic and Real Images: a Transferable and Multiple Consistency aided Fundus Image Enhancement Framework [61.74188977009786]
  We propose an end-to-end optimized teacher-student framework to simultaneously conduct image enhancement and domain adaptation. We also propose a novel multi-stage multi-attention guided enhancement network (MAGE-Net) as the backbones of our teacher and student network.
  arXiv  Detail & Related papers  (2023-02-23T06:16:15Z)
• A Keypoint Detection and Description Network Based on the Vessel Structure for Multi-Modal Retinal Image Registration [0.0]
  Multiple images with different modalities or acquisition times are often analyzed for the diagnosis of retinal diseases. Our method uses a convolutional neural network to extract features of the vessel structure in multi-modal retinal images.
  arXiv  Detail & Related papers  (2022-01-06T20:43:35Z)
• Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection [9.826027427965354]
  Recent advances have shown that the convolutional Neural Network (CNN) architectures can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection. We consider some contemporary CNN models for binary classification of breast histopathology images. We have validated the trained CNN networks on a publicly available BreaKHis dataset, for 200x and 400x magnified histopathology images.
  arXiv  Detail & Related papers  (2022-01-04T03:09:40Z)
• Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks [1.1601676598120785]
  Glaucoma is one of the leading causes of blindness worldwide. We propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms.
  arXiv  Detail & Related papers  (2021-06-25T10:53:01Z)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.