Towards the Localisation of Lesions in Diabetic Retinopathy

• URL: http://arxiv.org/abs/2012.11432v2
• Date: Tue, 2 Feb 2021 09:29:26 GMT
• Title: Towards the Localisation of Lesions in Diabetic Retinopathy
• Authors: Samuel Ofosu Mensah, Bubacarr Bah, Willie Brink
• Abstract summary: This study uses pre-trained weights from four state-of-the-art deep learning models to produce and compare localisation maps of diabetic retinopathy (DR) fundus images. InceptionV3 achieves the best performance with a test classification accuracy of 96.07%, and localise lesions better and faster than the other models.
• Score: 2.3204178451683264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional Neural Networks (CNNs) have successfully been used to classify diabetic retinopathy (DR) fundus images in recent times. However, deeper representations in CNNs may capture higher-level semantics at the expense of spatial resolution. To make predictions usable for ophthalmologists, we use a post-attention technique called Gradient-weighted Class Activation Mapping (Grad-CAM) on the penultimate layer of deep learning models to produce coarse localisation maps on DR fundus images. This is to help identify discriminative regions in the images, consequently providing evidence for ophthalmologists to make a diagnosis and potentially save lives by early diagnosis. Specifically, this study uses pre-trained weights from four state-of-the-art deep learning models to produce and compare localisation maps of DR fundus images. The models used include VGG16, ResNet50, InceptionV3, and InceptionResNetV2. We find that InceptionV3 achieves the best performance with a test classification accuracy of 96.07%, and localise lesions better and faster than the other models.

Related papers

• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing [0.0]
  This research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model for timely DR identification. The proposed model will detect various lesions from retinal images in the early stages.
  arXiv  Detail & Related papers  (2023-05-08T14:17:33Z)
• Improving Classification of Retinal Fundus Image Using Flow Dynamics Optimized Deep Learning Methods [0.0]
  Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes mellitus damaging the blood vessel network present in the retina. It can take some time to perform a DR diagnosis using color fundus pictures because experienced clinicians are required to identify the tumors in the imagery used to identify the illness.
  arXiv  Detail & Related papers  (2023-04-29T16:11:34Z)
• ROCT-Net: A new ensemble deep convolutional model with improved spatial resolution learning for detecting common diseases from retinal OCT images [0.0]
  This paper presents a new enhanced deep ensemble convolutional neural network for detecting retinal diseases from OCT images. Our model generates rich and multi-resolution features by employing the learning architectures of two robust convolutional models. Our experiments on two datasets and comparing our model with some other well-known deep convolutional neural networks have proven that our architecture can increase the classification accuracy up to 5%.
  arXiv  Detail & Related papers  (2022-03-03T17:51:01Z)
• Explainable multiple abnormality classification of chest CT volumes with AxialNet and HiResCAM [89.2175350956813]
  We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images. We propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality. We then aim to improve the model's learning through a novel mask loss that leverages HiResCAM and 3D allowed regions.
  arXiv  Detail & Related papers  (2021-11-24T01:14:33Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• Blindness (Diabetic Retinopathy) Severity Scale Detection [0.0]
  Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness. Timely diagnosis and treatment of DR are critical to avoid total loss of vision. We propose a novel deep learning based method for automatic screening of retinal fundus images.
  arXiv  Detail & Related papers  (2021-10-04T11:31:15Z)
• Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstruction [61.58601145792065]
  We learn invariant target-domain features by defining a novel self-supervised task based on retinal vessel image reconstructions. It can be shown that our approach outperforms existing domain strategies.
  arXiv  Detail & Related papers  (2021-07-20T09:44:07Z)
• DiaRet: A browser-based application for the grading of Diabetic Retinopathy with Integrated Gradients [0.0]
  Diabetes is a metabolic disorder that results from defects in autoimmune beta-cell destruction in Type 1, peripheral resistance to insulin action in Type 2 or, most commonly, both. Patients with long-standing diabetes often fall prey to Diabetic Retinopathy resulting in changes in the retina of the human eye, which may lead to loss of vision in extreme cases. The aim of this study is two-fold: (a) create deep learning models that were trained to grade degraded retinal fundus images and (b) to create a browser-based application that will aid in diagnostic procedures by highlighting the key features of the fundus image.
  arXiv  Detail & Related papers  (2021-03-15T16:19:56Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• 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)

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.