A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis
Bibliographic record
Abstract
The identification and early treatment of retinal disease can help to prevent loss of vision. Early diagnosis allows a greater range of treatment options and results in better outcomes. Optical coherence tomography (OCT) is a technology used by ophthalmologists to detect and diagnose certain eye conditions. In this paper, human retinal OCT images are classified into four classes using deep learning. Several image preprocessing techniques are employed to enhance the image quality. An augmentation technique, called generative adversarial network (GAN), is utilized in the Drusen and DME classes to address data imbalance issues, resulting in a total of 130,649 images. A lightweight optimized compact convolutional transformers (OCCT) model is developed by conducting an ablation study on the initial CCT model for categorizing retinal conditions. The proposed OCCT model is compared with two transformer-based models: vision Transformer (ViT) and Swin Transformer. The models are trained and evaluated with 32 × 32 sized images of the GAN-generated enhanced dataset. Additionally, eight transfer learning models are presented with the same input images to compare their performance with the OCCT model. The proposed model's stability is assessed by decreasing the number of training images and evaluating the performance. The OCCT model's accuracy is 97.09%, and it outperforms the two transformer models. The result further indicates that the OCCT model sustains its performance, even if the number of images is reduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".