Diabetic Retinopathy Screening Using Custom-Designed Convolutional Neural Network

• URL: http://arxiv.org/abs/2110.03877v1
• Date: Fri, 8 Oct 2021 03:30:45 GMT
• Title: Diabetic Retinopathy Screening Using Custom-Designed Convolutional Neural Network
• Authors: Fahman Saeed, Muhammad Hussain, Senior Member, IEEE, Hatim A Aboalsamh, Senior Member, IEEE, Fadwa Al Adel, Adi Mohammed Al Owaifeer
• Abstract summary: The prevalence of diabetic retinopathy (DR) has reached 34.6% worldwide and is a major cause of blindness among middle-aged diabetic patients. Regular DR screening using fundus photography helps detect its complications and prevent its progression to advanced levels. The existing CNN-based methods use either pre-trained CNN models or a brute force approach to design new CNN models, which are not customized to the complexity of fundus images.
• Score: 1.3069410690405037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The prevalence of diabetic retinopathy (DR) has reached 34.6% worldwide and is a major cause of blindness among middle-aged diabetic patients. Regular DR screening using fundus photography helps detect its complications and prevent its progression to advanced levels. As manual screening is time-consuming and subjective, machine learning (ML) and deep learning (DL) have been employed to aid graders. However, the existing CNN-based methods use either pre-trained CNN models or a brute force approach to design new CNN models, which are not customized to the complexity of fundus images. To overcome this issue, we introduce an approach for custom-design of CNN models, whose architectures are adapted to the structural patterns of fundus images and better represent the DR-relevant features. It takes the leverage of k-medoid clustering, principal component analysis (PCA), and inter-class and intra-class variations to automatically determine the depth and width of a CNN model. The designed models are lightweight, adapted to the internal structures of fundus images, and encode the discriminative patterns of DR lesions. The technique is validated on a local dataset from King Saud University Medical City, Saudi Arabia, and two challenging benchmark datasets from Kaggle: EyePACS and APTOS2019. The custom-designed models outperform the famous pre-trained CNN models like ResNet152, Densnet121, and ResNeSt50 with a significant decrease in the number of parameters and compete well with the state-of-the-art CNN-based DR screening methods. The proposed approach is helpful for DR screening under diverse clinical settings and referring the patients who may need further assessment and treatment to expert ophthalmologists.

Related papers

• Enhancing Diabetic Retinopathy Detection with CNN-Based Models: A Comparative Study of UNET and Stacked UNET Architectures [0.0]
  Diabetic Retinopathy DR is a severe complication of diabetes. Damaged or abnormal blood vessels can cause loss of vision. The need for massive screening of a large population of diabetic patients has generated an interest in a computer-aided fully automatic diagnosis of DR. Deep learning frameworks, particularly convolutional neural networks CNNs, have shown great interest and promise in detecting DR by analyzing retinal images.
  arXiv  Detail & Related papers  (2024-11-02T14:02:45Z)
• KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation [46.57880203321858]
  We propose a novel network (KaLDeX) for vascular segmentation leveraging a Kalman filter based linear deformable cross attention (LDCA) module. Our approach is based on two key components: Kalman filter (KF) based linear deformable convolution (LD) and cross-attention (CA) modules. The proposed method is evaluated on retinal fundus image datasets (DRIVE, CHASE_BD1, and STARE) as well as the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-10-28T16:00:42Z)
• Classification of Diabetic Retinopathy using Pre-Trained Deep Learning Models [0.0]
  Diabetic Retinopathy (DR) stands as the leading cause of blindness globally, particularly affecting individuals between the ages of 20 and 70. This paper presents a Computer-Aided Diagnosis (CAD) system designed for the automatic classification of retinal images into five distinct classes: Normal, Mild, Moderate, Severe, and Proliferative Diabetic Retinopathy (PDR)
  arXiv  Detail & Related papers  (2024-03-29T01:11:56Z)
• Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition [59.28732531600606]
  We propose a framework named textbfClass textbfAttention to textbfREgions of the lesion (CARE) to handle data imbalance issues. The CARE framework needs bounding boxes to represent the lesion regions of rare diseases. Results show that the CARE variants with automated bounding box generation are comparable to the original CARE framework.
  arXiv  Detail & Related papers  (2023-07-19T15:19:02Z)
• 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)
• DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images [51.27125547308154]
  We organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022) The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge.
  arXiv  Detail & Related papers  (2023-04-05T12:04:55Z)
• An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images [4.640835690336653]
  We propose an ensemble method to automatically grade Diabetic retinopathy (DR) images available from Diabetic Retinopathy Analysis Challenge (DRAC) 2022. First, we adopt the state-of-the-art classification networks, and train them to grade UW- OCTA images with different splits of the available dataset. Ultimately, we obtain 25 models, of which, the top 16 models are selected and ensembled to generate the final predictions.
  arXiv  Detail & Related papers  (2022-12-12T22:06:47Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Towards the Localisation of Lesions in Diabetic Retinopathy [2.3204178451683264]
  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.
  arXiv  Detail & Related papers  (2020-12-21T15:39:17Z)
• 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.