Diabetic Retinopathy Classification Using a Modified Xception Architecture
Bibliographic record
Abstract
Diabetic retinopathy (DR) is one of the major causes of blindness worldwide. With proper treatment, early diagnosis of DR can prevent the progression of the disease. In this paper, we present a new feature extraction method using a modified Xception architecture for the diagnosis of DR disease. The proposed method is based on deep layer aggregation that combines multilevel features from different convolutional layers of Xception architecture. The extracted features are subsequently fed into a multi-layer perceptron (MLP) to be trained for DR severity classification. The performance of the proposed approach was assessed with four deep feature extractors, including Inception V3,MobileNet, and ResNet50 and original Xception architecture. Compared with typical Xception architecture, the aggregation of deep CNN layers can effectively fuse deep features and improve the learning process. Additionally, a transfer learning strategy and hyper-parameter tuning are adopted to further improve the overall classification performance. The performance of the proposed model was validated on the Kaggle APTOS 2019 contest dataset. Experiments demonstrate that the modified Xception deep feature extractor improves DR classification with a classification accuracy of 83.09% versus 79.59%, sensitivity of 88.24% versus 82.35% and specificity of 87.00% versus 86.32% when compared with the original Xception architecture.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".