Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification
Bibliographic record
Abstract
A machine learning model is introduced to recognize the severity level of the Diabetic Retinopathy (DR), a disease observed in the people suffering from diabetes for a long time and is one of the causes of vision loss and blindness. Major objective of this approach is to generate an effective feature representation of the fundus images so that the level of severity can be identified with less effort and using limited number of samples for training. Color fundus images of the retina are collected, preprocessed and deep features are extracted by feeding them to a deep Convolutional Network, Neural Architecture Search Network (NASNet) which searches for the best convolutional layer (or "cell") in NASNet search space. The representations of retinal images in deep space are given as input to the classification model to get the severity level of the disease. The proposed model is applied on the benchmark APTOS 2019 retinal fundus image dataset to evaluate the performance of the proposed model. Our experimental studies indicate that -Support Vector Machine (-SVM) when trained using the projected deep features leads to an improvement in accuracy compared to other machine learning models for fundus image classification. In addition, from the experimental studies we understand that deep features from NASNet give better representation compared to the handcrafted features and features obtained using other projections. We observe that deep features transformed using t-distributed stochastic neighbor embedding (t-SNE) gives more discriminative representations of retinal images and help to achieve an accuracy of 77.90%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".