Integrating lightweight convolutional neural network with entropy-informed channel attention and adaptive spatial attention for OCT-based retinal disease classification
Bibliographic record
Abstract
This article proposes an effective and lightweight contextual convolutional neural network architecture called LOCT-Net for classifying retinal diseases. The LOCT-Net adopts nested residual blocks to capture the local patterns from the optical coherence tomography brightness scans and facilitate gradient flow throughout the network. The multi-scale feature enhancement module incorporates dilation-integrated depthwise strip convolutions to extract fine-grained contextual patterns with an expanded receptive field and a gating mechanism. The extracted features are then refined by a novel feature refinement network consisting of the entropy-informed channel attention module, followed by the adaptive spatial attention module. The entropy-informed channel attention module uses the frequency distribution of pixel values to compute attention weights for spatial analysis. The adaptive spatial attention module focuses on relevant clinical regions and further refines the feature maps in a multi-kernel setting. Additionally, post-explainable artificial intelligence methods are used to provide explanations of LOCT-Net’s decision-making and predictions. The LOCT-Net model has been evaluated on six benchmark datasets, demonstrating an efficient balance between performance and computational cost. With just 2.32 M trainable parameters, the proposed model addresses key challenges in retinal disease classification tasks using OCT B-scans and surpasses previous state-of-the-art methods, achieving an F1 score of 92.98%, 92.34%, 100%, 99.58%, 94.50%, and 97.14% in the OCTID, OCTDL, DUKE, SD-OCT Noor, NEH, and UCSD datasets, respectively. • Context-guided CNN for retinal disease classification with 2.32M parameters. • EICA boosts classification by prioritizing high-entropy channels to capture key features. • Multi-kernel Adaptive Spatial Attention and DsConv capture multi-scale features. • XAI methods like Grad-CAM and LIME provide visual interpretation of decision-making. • Obtains an average accuracy of 96.46% across six benchmark OCT B-scan datasets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".