Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning

• URL: http://arxiv.org/abs/2507.19199v1
• Date: Fri, 25 Jul 2025 12:09:27 GMT
• Title: Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning
• Authors: Abdul Hannan, Zahid Mahmood, Rizwan Qureshi, Hazrat Ali,
• Abstract summary: In this work, we combine global attention block (GAB) and category attention block (CAB) into the deep learning model.<n>Our proposed approach is based on an attention mechanism-based deep learning model that employs three pre-trained networks.<n>The proposed approach achieves competitive performance that is at par with recently reported works on DR classification.
• Score: 2.856144231792095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic classification of Diabetic Retinopathy (DR) can assist ophthalmologists in devising personalized treatment plans, making it a critical component of clinical practice. However, imbalanced data distribution in the dataset becomes a bottleneck in the generalization of deep learning models trained for DR classification. In this work, we combine global attention block (GAB) and category attention block (CAB) into the deep learning model, thus effectively overcoming the imbalanced data distribution problem in DR classification. Our proposed approach is based on an attention mechanism-based deep learning model that employs three pre-trained networks, namely, MobileNetV3-small, Efficientnet-b0, and DenseNet-169 as the backbone architecture. We evaluate the proposed method on two publicly available datasets of retinal fundoscopy images for DR. Experimental results show that on the APTOS dataset, the DenseNet-169 yielded 83.20% mean accuracy, followed by the MobileNetV3-small and EfficientNet-b0, which yielded 82% and 80% accuracies, respectively. On the EYEPACS dataset, the EfficientNet-b0 yielded a mean accuracy of 80%, while the DenseNet-169 and MobileNetV3-small yielded 75.43% and 76.68% accuracies, respectively. In addition, we also compute the F1-score of 82.0%, precision of 82.1%, sensitivity of 83.0%, specificity of 95.5%, and a kappa score of 88.2% for the experiments. Moreover, in our work, the MobileNetV3-small has 1.6 million parameters on the APTOS dataset and 0.90 million parameters on the EYEPACS dataset, which is comparatively less than other methods. The proposed approach achieves competitive performance that is at par with recently reported works on DR classification.

Related papers

• Robust Five-Class and binary Diabetic Retinopathy Classification Using Transfer Learning and Data Augmentation [1.3492288506683114]
  This paper presents a robust deep learning framework for both binary and five-class Diabetic retinopathy (DR) classification.<n>For binary classification, our proposed model achieves a state-of-the-art accuracy of 98.9%, with a precision of 98.6%, recall of 99.3%, F1-score of 98.9%, and an AUC of 99.4%.<n>In the more challenging five-class severity classification task, our model obtains a competitive accuracy of 84.6% and an AUC of 94.1%, outperforming several existing approaches.
  arXiv  Detail & Related papers  (2025-07-23T01:52:27Z)
• Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans [0.0]
  We propose a robust methodology for automatic segmentation of infected lung regions in COVID-19 CT scans using convolutional neural networks.<n>The approach is based on a modified U-Net architecture enhanced with attention mechanisms, data augmentation, and postprocessing techniques.
  arXiv  Detail & Related papers  (2025-05-18T08:27:12Z)
• Deep Learning Approach for Enhancing Oral Squamous Cell Carcinoma with LIME Explainable AI Technique [0.0]
  The dataset consisted of 5192 images (2435 Normal and 2511 OSCC)<n>We selected four deep-learning architectures for evaluation in the present study: ResNet101, DenseNet121, VGG16, and EfficientnetB3.<n> EfficientNetB3 was found to be the best, with an accuracy of 98.33% and F1 score (0.9844), and it took remarkably less computing power in comparison with other models.
  arXiv  Detail & Related papers  (2024-11-21T14:53:59Z)
• Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure [53.76752789814785]
  DumplingGNN is a hybrid Graph Neural Network architecture specifically designed for predicting ADC payload activity based on chemical structure. We evaluate it on a comprehensive ADC payload dataset focusing on DNA Topoisomerase I inhibitors. It demonstrates exceptional accuracy (91.48%), sensitivity (95.08%), and specificity (97.54%) on our specialized ADC payload dataset.
  arXiv  Detail & Related papers  (2024-09-23T17:11:04Z)
• Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning [79.43940012723539]
  ADCLR is a self-supervised learning framework for learning accurate and dense vision representation. Our approach achieves new state-of-the-art performance for contrastive methods.
  arXiv  Detail & Related papers  (2023-06-23T07:38:09Z)
• SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis [3.1542695050861544]
  Deep learning-based supervised methods are widely popular in medical image analysis. They require a large amount of training data and face issues in generalisability to unseen datasets. We propose to explore patch-level instance-group discrimination and penalisation of inter-class variation using additive angular margin.
  arXiv  Detail & Related papers  (2023-05-31T21:28:08Z)
• Novel Epileptic Seizure Detection Techniques and their Empirical Analysis [2.3301643766310374]
  We use the tri-dimensionality reduction algorithm, Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA) The proposed framework is tested on the Bonn dataset. The simulation results provide 100% accuracy for the LDA and NB combination.
  arXiv  Detail & Related papers  (2023-02-17T20:58:17Z)
• CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis [37.41181188499616]
  Deep learning techniques can help in the faster detection of COVID-19 cases and monitoring of disease progression. Five different datasets were used to construct a representative dataset of 23 799 CXRs for model training. A U-Net-based model was developed to identify a clinically relevant region of the CXR.
  arXiv  Detail & Related papers  (2022-10-11T13:30:34Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)

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.