A novel few-shot classification framework for diabetic retinopathy detection and grading

Murugappan, M.; Prakash, N. B.; Jeya, R.; Mohanarathinam, A.; Hemalakshmi, G. R.; Mahmud, Mufti

doi:10.1016/j.measurement.2022.111485

Cited by 28 publications

(8 citation statements)

References 35 publications

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“…The Macro-F1 score is defined as shown in Eq. [10] ( 40 , 41 ). The Macro-F1 score also lies between 0.0 and 1.0, with the smallest value [0] indicating the worst performance of the classifier, and the highest value [1] indicating the best performance of the classifier.…”

Section: Resultsmentioning

confidence: 99%

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A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification

Liu,

Dong,

et al. 2024

Quant Imaging Med Surg

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Background Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images. Methods The difficulty-aware (Da) method operates by dynamically modifying the cross-entropy loss function applied to learning tasks. This methodology has the ability to intelligently down-weight simpler tasks, while simultaneously prioritizing more challenging tasks. These adjustments occur automatically and aim to optimize the learning process. Additionally, the task-augmentation (Ta) method is used to enhance the meta-training process by augmenting the number of tasks through image rotation and improving the feature-extraction capability. To implement the expansion of the meta-training tasks, various task instances can be sampled during the meta-training stage. Ultimately, the proposed Ta method was introduced to optimize the initialization parameters and enhance the meta-generalization performance of the model. The DaTa-ML model showed promising results by effectively addressing the challenges associated with few-shot DR classification. Results The Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 blindness detection data set was used to evaluate the DaTa-ML model. The results showed that with only 1% of the training data (5-way, 20-shot) and a single update step (training time reduced by 90%), the DaTa-ML model had an accuracy rate of 89.6% on the test data, which is a 1.7% improvement over the transfer-learning method [i.e., residual neural network (ResNet)50 pre-trained on ImageNet], and a 16.8% improvement over scratch-built models (i.e., ResNet50 without pre-trained weights), despite having fewer trainable parameters (the parameters used by the DaTa-ML model are only 0.47% of the ResNet50 parameters). Conclusions The DaTa-ML model provides a more efficient DR classification solution with little annotated data and has significant advantages over state-of-the-art methods. Thus, it could be used to guide and assist ophthalmologists to determine the severity of DR.

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Section: Resultsmentioning

confidence: 99%

“…We drew comparisons with two similar pieces of research to illustrate the unique aspects of our work. First, diabetes retinopathy network (DRNet) ( 41 ) is a prototype network for DR detection and grading. This network constructs a meta-classifier between basic classifiers using the attention mechanism.…”

Section: Discussionmentioning

confidence: 99%

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification

Liu,

Dong,

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

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“…The model's objective is to ascertain the level of severity in the patient's eye, and it will be valuable in precisely categorizing the severity of DR. The study conducted by Murugappan et al [33] utilized a Few-Shot Learning approach to develop a tool for detecting and evaluating DR. The approach employs an attention mechanism and episodic learning to train the model with limited training data.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform

Mutawa,

Al-Sabti,

Raizada

et al. 2024

Preprint

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Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.

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“…The use of deep learning for medical image analysis has risen dramatically in recent years [ 11 , 12 , 13 , 14 ]. Automatic classification and detection of acute ICH using deep learning algorithms is presented in [ 15 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques

Umapathy,

Murugappan,

Bharathi

et al. 2023

Diagnostics

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Diagnosing Intracranial Hemorrhage (ICH) at an early stage is difficult since it affects the blood vessels in the brain, often resulting in death. We propose an ensemble of Convolutional Neural Networks (CNNs) combining Squeeze and Excitation–based Residual Networks with the next dimension (SE-ResNeXT) and Long Short-Term Memory (LSTM) Networks in order to address this issue. This research work primarily used data from the Radiological Society of North America (RSNA) brain CT hemorrhage challenge dataset and the CQ500 dataset. Preprocessing and data augmentation are performed using the windowing technique in the proposed work. The ICH is then classified using ensembled CNN techniques after being preprocessed, followed by feature extraction in an automatic manner. ICH is classified into the following five types: epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural. A gradient-weighted Class Activation Mapping method (Grad-CAM) is used for identifying the region of interest in an ICH image. A number of performance measures are used to compare the experimental results with various state-of-the-art algorithms. By achieving 99.79% accuracy with an F-score of 0.97, the proposed model proved its efficacy in detecting ICH compared to other deep learning models. The proposed ensembled model can classify epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural hemorrhages with an accuracy of 99.89%, 99.65%, 98%, 99.75%, and 99.88%. Simulation results indicate that the suggested approach can categorize a variety of intracranial bleeding types. By implementing the ensemble deep learning technique using the SE-ResNeXT and LSTM models, we achieved significant classification accuracy and AUC scores.

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A novel few-shot classification framework for diabetic retinopathy detection and grading

Cited by 28 publications

References 35 publications

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification

A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform

Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques

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