A Comparative Study of Convolutional Layer Depths and Activation Functions in the Detection and Classification of Diabetic Retinopathy
Bibliographic record
Abstract
Traditional diabetic retinopathy diagnosis is a timeconsuming approach and is limited by the availability of specialists. Deep learning systems are a solution to the constraints, as they can efficiently and accurately detect anomalies in large datasets of retinal images. This work presents a comparative study of convolutional neural networks trained from scratch compared to traditional pretrained models in the field of diabetic retinopathy classification. In this work, four mid-range models are presented that display how different combinations of convolutions, activation functions, and layer depths can be used to achieve high performance in diabetic retinopathy classification without large-scale pretraining. Furthermore, the test performances of these models are compared to several pretrained models in the field to investigate how different architectures affect the accuracy of binary classification. The models are trained and tested on open-source datasets such as Eyepacs, Aptos, Aptos (Gaussian Filtered), and Messidor DR. The highest test accuracy achieved is 87.60 % for the presence of diabetic retinopathy versus the absence of diabetic retinopathy and 88.38 % for the distinction between non-proliferative diabetic retinopathy and proliferative diabetic retinopathy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".