Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms

• URL: http://arxiv.org/abs/2504.03439v1
• Date: Fri, 04 Apr 2025 13:30:21 GMT
• Title: Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms
• Authors: Mohammad Reza Yousefi, Ali Bakrani, Amin Dehghani,
• Abstract summary: Diabetic Retinopathy (DR) is a serious and common complication of diabetes.<n>Traditional diabetes diagnosis methods often utilize convolutional neural networks (CNNs) to extract visual features from retinal images.<n>This study investigates the application of transfer learning (TL) to enhance ML model performance in DR detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diabetic Retinopathy (DR) is a serious and common complication of diabetes, caused by prolonged high blood sugar levels that damage the small retinal blood vessels. If left untreated, DR can progress to retinal vein occlusion and stimulate abnormal blood vessel growth, significantly increasing the risk of blindness. Traditional diabetes diagnosis methods often utilize convolutional neural networks (CNNs) to extract visual features from retinal images, followed by classification algorithms such as decision trees and k-nearest neighbors (KNN) for disease detection. However, these approaches face several challenges, including low accuracy and sensitivity, lengthy machine learning (ML) model training due to high data complexity and volume, and the use of limited datasets for testing and evaluation. This study investigates the application of transfer learning (TL) to enhance ML model performance in DR detection. Key improvements include dimensionality reduction, optimized learning rate adjustments, and advanced parameter tuning algorithms, aimed at increasing efficiency and diagnostic accuracy. The proposed model achieved an overall accuracy of 84% on the testing dataset, outperforming prior studies. The highest class-specific accuracy reached 89%, with a maximum sensitivity of 97% and an F1-score of 92%, demonstrating strong performance in identifying DR cases. These findings suggest that TL-based DR screening is a promising approach for early diagnosis, enabling timely interventions to prevent vision loss and improve patient outcomes.

Related papers

• Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity [43.108040967674185]
  Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD)<n>This study builds on previous work that utilised machine learning (ML) to detect UTIs in PLWD.
  arXiv  Detail & Related papers  (2025-02-18T12:01:55Z)
• Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Clinical Pathology Analysis [34.199766079609795]
  Pathological diagnosis is vital for determining disease characteristics, guiding treatment, and assessing prognosis.<n>Traditional pure vision models face challenges of redundant feature extraction.<n>Existing large vision-language models (LVLMs) are limited by input resolution constraints, hindering their efficiency and accuracy.<n>We propose two innovative strategies: the mixed task-guided feature enhancement, and the prompt-guided detail feature completion.
  arXiv  Detail & Related papers  (2024-12-12T18:07:23Z)
• A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis [3.84521268332112]
  Chronic Kidney Disease (CKD) represents a significant global health challenge, characterized by the progressive decline in renal function.<n>Our study delves into the application of cutting-edge transfer learning models for the early detection of CKD.
  arXiv  Detail & Related papers  (2024-12-12T17:18:49Z)
• Enhancing Diabetic Retinopathy Diagnosis: A Lightweight CNN Architecture for Efficient Exudate Detection in Retinal Fundus Images [0.0]
  This paper introduces a novel, lightweight convolutional neural network architecture tailored for automated exudate detection. We have incorporated domain-specific data augmentations to enhance the model's generalizability. Our model achieves an impressive F1 score of 90%, demonstrating its efficacy in the early detection of diabetic retinopathy through fundus imaging.
  arXiv  Detail & Related papers  (2024-08-13T10:13:33Z)
• Deep Learning Innovations in Diagnosing Diabetic Retinopathy: The Potential of Transfer Learning and the DiaCNN Model [14.643107563426701]
  Diabetic retinopathy (DR) is a significant cause of vision impairment. Traditional diagnostic methods, relying on human interpretation, face challenges in terms of accuracy and efficiency. We introduce a novel method that offers superior precision in DR diagnosis, compared to these traditional methods.
  arXiv  Detail & Related papers  (2024-01-25T07:24:06Z)
• Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection [0.0]
  Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment. Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance. This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection.
  arXiv  Detail & Related papers  (2023-09-02T04:42:08Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan [40.51754649947294]
  The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. The model's diagnostic performance was compared with clinicians's performance. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system augmentation.
  arXiv  Detail & Related papers  (2023-02-02T08:45:17Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)
• Predictive Analysis of Diabetic Retinopathy with Transfer Learning [0.0]
  This paper studies the performance of CNN architectures for Diabetic Retinopathy Classification with the help of Transfer Learning. The results indicate that Transfer Learning with ImageNet weights using VGG 16 model demonstrates the best classification performance with the best Accuracy of 95%.
  arXiv  Detail & Related papers  (2020-11-08T18:54:57Z)
• A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
  People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
  arXiv  Detail & Related papers  (2020-08-22T07:48:04Z)

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.