| --- |
| license: mit |
| language: |
| - en |
| metrics: |
| - precision |
| - f1 |
| - recall |
| --- |
| # Model Card: EfficientNet-B0--B7 for Four-Class SD-OCT Retinal Image Classification |
|
|
| **Study:** *Within-Family EfficientNet Scaling under Fixed-Resolution |
| Transfer Learning for Spectral-Domain Optical Coherence Tomography |
| Retinal Disease Classification*\ |
| **Researcher:** Shkëlqim Sherifi, University of Tetova\ |
| **Model family:** ImageNet-pretrained EfficientNet-B0, B1, B2, B3, B4, |
| B5, B6, and B7\ |
| **Task:** Four-class classification of spectral-domain optical coherence |
| tomography (SD-OCT) retinal images\ |
| **Status:** Research prototype; not clinically validated\ |
| **Primary result:** EfficientNet-B1 achieved the highest reported test |
| accuracy (96.95%) in this experiment. |
|
|
| > **Important use restriction:** These models are research models, not |
| > medical devices. They must not be used as the sole basis for |
| > diagnosis, screening, triage, treatment, or other patient-management |
| > decisions. Clinical use would require independent validation, |
| > appropriate regulatory review, and evaluation in the intended clinical |
| > setting. |
|
|
| ------------------------------------------------------------------------ |
|
|
| ## 1. Model details |
|
|
| ### Model description |
|
|
| This study compares the eight members of the EfficientNet family, B0 |
| through B7, under a shared fixed-resolution transfer-learning protocol. |
| Each backbone was initialized with ImageNet-pretrained weights and |
| adapted to classify SD-OCT images into four dataset labels: **CNV**, |
| **DME**, **Drusen**, and **Normal**. |
|
|
| The experiment was designed to assess how scaling within the |
| EfficientNet family affects classification performance and computational |
| cost when the input resolution is held constant at 224 × 224 pixels. |
| Results therefore describe this particular dataset and training |
| protocol; they do not establish that a larger EfficientNet is generally |
| better or worse for OCT. |
|
|
| ### Model variants and parameter counts |
|
|
| The paper reports experimentally instantiated backbone parameters and |
| total parameters after adding the classification head. |
|
|
| Variant Backbone parameters Total model parameters |
| ----------------- --------------------- ------------------------ |
| EfficientNet-B0 4.05 million 4.84 million |
| EfficientNet-B1 6.58 million 7.37 million |
| EfficientNet-B2 7.77 million 8.63 million |
| EfficientNet-B3 10.78 million 11.71 million |
| EfficientNet-B4 17.67 million 18.73 million |
| EfficientNet-B5 28.51 million 29.70 million |
| EfficientNet-B6 40.96 million 42.28 million |
| EfficientNet-B7 64.10 million 65.54 million |
|
|
| ### Intended use |
|
|
| - Research on transfer learning for four-class SD-OCT image |
| classification. |
| - Reproducible comparison of EfficientNet model scaling at a fixed 224 |
| × 224 input resolution. |
| - Educational and methodological investigations into performance, |
| class-wise errors, interpretability, and computational trade-offs. |
| |
| ### Out-of-scope use |
|
|
| - Clinical diagnosis or exclusion of retinal disease. |
| - Patient screening, referral, triage, treatment selection, or |
| prognosis. |
| - Use on fundus photographs, other imaging modalities, or OCT datasets |
| with different acquisition and labeling procedures without new |
| validation. |
| - Autonomous clinical deployment or use as a replacement for qualified |
| eye-care professionals. |
| |
| ## 2. Intended prediction |
|
|
| The model produces a four-class probability distribution over the |
| following labels: |
|
|
| Output label Meaning in the dataset |
| -------------- ------------------------------ |
| `CNV` Choroidal neovascularization |
| `DME` Diabetic macular edema |
| `Drusen` Drusen |
| `Normal` Normal OCT image |
|
|
| The class order used by the classification head is **CNV, DME, Drusen, |
| Normal**. Implementations should preserve this order when interpreting |
| the output vector. |
|
|
| ## 3. Training data |
|
|
| ### Dataset and scale |
|
|
| The study used the four-class Kermany SD-OCT dataset, with **109,309 |
| images** in the supplied training, validation, and test partitions. |
|
|
| Class Training Validation Test Total |
| ----------- ------------ ------------ ------------ ------------- |
| CNV 26,218 7,491 3,746 37,455 |
| DME 8,118 2,319 1,161 11,598 |
| Drusen 6,206 1,773 887 8,866 |
| Normal 35,973 10,278 5,139 51,390 |
| **Total** **76,515** **21,861** **10,933** **109,309** |
|
|
| The class distribution is imbalanced: Normal and CNV are more |
| represented than Drusen and DME. The training procedure used class |
| weights to address this imbalance. |
|
|
| ### Split provenance and data-integrity caveats |
|
|
| The study retained the partitions supplied with the dataset release. The |
| paper does **not** establish that patient-level separation was |
| independently verified, and it does not report an independent |
| duplicate-image audit across the exact partitions used. Consequently, |
| the reported test results should not be interpreted as proof of |
| generalization to unseen patients or independent clinical sites. These |
| are unresolved validation questions, not evidence that leakage |
| definitely occurred. |
|
|
| The results are tied to this dataset and its label definitions. |
| Dataset-level performance may not transfer to images from other devices, |
| clinics, patient populations, or acquisition protocols. |
|
|
| ## 4. Input processing |
|
|
| - **Input size:** 224 × 224 pixels. |
| - **Image channels:** Grayscale OCT images are converted to three |
| channels by replicating the grayscale channel. |
| - **Backbone initialization:** ImageNet-pretrained EfficientNet |
| weights (`weights='imagenet'`, `include_top=False`). |
| - **Preprocessing:** The EfficientNet model's embedded preprocessing |
| was retained. |
| - **Training-only augmentation:** Random horizontal flip, random |
| rotation up to ±5°, translation up to ±5%, zoom up to ±5%, and |
| contrast variation with factor 0.15. Vertical flipping was not used. |
| |
| For inference, the same resize, channel-replication, and model |
| preprocessing conventions used in training should be followed. The paper |
| does not specify a universally applicable preprocessing pipeline for |
| arbitrary external OCT exports; deployments should document and verify |
| their exact image-loading and resizing implementation. |
|
|
| ## 5. Architecture and training procedure |
|
|
| ### Classification head |
|
|
| The ImageNet classification top was removed. The task-specific head |
| consisted of: |
|
|
| 1. Global average pooling. |
| 2. Dense layer with 512 units and ReLU activation. |
| 3. Batch normalization. |
| 4. Dropout (0.5). |
| 5. Dense layer with 256 units and ReLU activation. |
| 6. Batch normalization. |
| 7. Dropout (0.3). |
| 8. Four-unit softmax output layer. |
|
|
| ### Two-stage transfer learning |
|
|
| 1. **Stage 1 --- frozen backbone:** Train the classification head while |
| keeping the pretrained backbone frozen; maximum 15 epochs. |
| 2. **Stage 2 --- partial fine-tuning:** Unfreeze approximately the |
| upper 50% of the backbone and fine-tune jointly with the head; |
| maximum 30 epochs. The optimizer was reinitialized for this stage |
| and the learning rate was reduced relative to Stage 1. |
| |
| Early stopping and learning-rate reduction on validation loss were used. |
| Stage 1 used early-stopping patience 5 and learning-rate-reduction |
| patience 2; Stage 2 used patience 6 and 3, respectively. The paper |
| reports AdamW, class-weighted categorical cross-entropy with label |
| smoothing (α = 0.1), and a common random seed of 42. Exact learning-rate |
| and weight-decay values should be copied from the authors' training |
| configuration before attempting a bit-for-bit reproduction; they are not |
| reproduced here because the available paper extraction does not reliably |
| preserve the exponent formatting. |
|
|
| ### Training environment |
|
|
| - TensorFlow 2.20.0 with Keras. |
| - Google Colab. |
| - NVIDIA Tesla T4 GPU with 16 GB memory. |
| - Mixed precision: float16 computation with float32 output. |
| - Batch size: 16. |
| - Class weights reported approximately as CNV 0.73, DME 2.36, Drusen |
| 3.08, and Normal 0.53. |
| |
| ## 6. Reported results |
|
|
| ### Main finding |
|
|
| The study found that test accuracy did not increase monotonically with |
| EfficientNet scale. **EfficientNet-B1 achieved the highest reported test |
| accuracy, 96.95%, and the highest reported Drusen F1 score, 0.8780.** |
| EfficientNet-B7 reached 96.73% accuracy and the highest Drusen recall, |
| 0.9177, but had a lower Drusen F1 score of 0.8720 and substantially |
| higher parameter count and CPU latency. |
|
|
| ----------------------------------------------------------------------------- |
| Variant Test accuracy Drusen F1 Drusen recall Total |
| parameters |
| ----------------- -------------- -------------- -------------- -------------- |
| EfficientNet-B0 95.93% --- --- 4.84M |
| |
| EfficientNet-B1 **96.95%** **0.8780** --- 7.37M |
|
|
| EfficientNet-B7 96.73% 0.8720 **0.9177** 65.54M |
| ----------------------------------------------------------------------------- |
|
|
| Dashes mean that the corresponding value is not included in this concise |
| summary; they should not be interpreted as zero or as unavailable in the |
| full paper. |
|
|
| ### Class-wise observations |
|
|
| - Drusen was the lowest-F1 class for all eight variants. |
| - Across the evaluated models, the reported F1 ranking was **Normal \> |
| CNV \> DME \> Drusen**. |
| - Drusen accounted for 82.2% of the summed squared deviation in |
| class-specific F1 across the family, according to the paper's |
| analysis. |
| - B7's higher Drusen recall did not produce the best Drusen F1 because |
| its precision was lower than B1's. |
| - Larger models incurred substantially greater computational cost |
| without a monotonic improvement in test accuracy. |
| |
| These findings support choosing a model based on the required balance of |
| accuracy, class-specific errors, latency, memory, and deployment |
| constraints---not parameter count alone. They do not demonstrate |
| clinical superiority of B1 over other models. |
|
|
| ## 7. CPU inference measurements reported in the paper |
|
|
| Measurements were made on an Intel Core i7-6700HQ 2.60 GHz CPU, Windows |
| 10, batch size 1, float32; the procedure used 50 warm-up runs and 200 |
| timed runs. The paper notes that these measurements are approximate and |
| may be affected by background processes. |
|
|
| -------------------------------------------------------------------------- |
| Variant Mean latency Median latency Throughput |
| (ms/image) (ms/image) (images/s) |
| ----------------- ------------------ ------------------ ------------------ |
| EfficientNet-B0 407.48 345.51 2.454 |
| |
| EfficientNet-B1 512.79 474.43 1.950 |
|
|
| EfficientNet-B2 479.46 474.48 2.086 |
|
|
| EfficientNet-B3 544.27 543.42 1.837 |
|
|
| EfficientNet-B4 683.46 681.20 1.463 |
|
|
| EfficientNet-B5 844.98 841.37 1.183 |
|
|
| EfficientNet-B6 971.32 970.17 1.030 |
|
|
| EfficientNet-B7 1,247.88 1,232.05 0.801 |
| -------------------------------------------------------------------------- |
|
|
| These values are specific to the reported hardware, software, precision, |
| and benchmark procedure. They are not guaranteed production latency |
| figures. |
|
|
| ## 8. Evaluation |
|
|
| The paper reports evaluation using aggregate classification metrics, |
| including accuracy, weighted F1, macro F1, balanced accuracy, Matthews |
| correlation coefficient (MCC), and Cohen's kappa. The analyses also |
| include class-wise precision, recall and F1, confusion matrices, ROC and |
| precision--recall analyses, training dynamics, predictive-probability |
| structure, Grad-CAM, and t-SNE representations. |
|
|
| The results summarized here are retrospective test-set results from the |
| dataset used in the study. The paper does not establish prospective |
| clinical performance or external validation on independently collected |
| clinical data. |
|
|
| ## 9. Limitations and risks |
|
|
| 1. **No established clinical validation.** This is a research |
| comparison, not a clinically validated diagnostic system. |
| 2. **Generalization is unknown.** Performance on other hospitals, OCT |
| devices, acquisition protocols, and patient populations has not been |
| established by this experiment. |
| 3. **Patient-level split verification is unresolved.** The study |
| retained supplied dataset partitions but did not independently |
| establish patient-level separation. |
| 4. **Duplicate audit is unresolved.** The exact partitions used were |
| not independently audited for duplicate images across train, |
| validation, and test. |
| 5. **Class imbalance and Drusen errors.** Drusen had the weakest F1 |
| across all variants, so aggregate accuracy can obscure clinically |
| important class-specific weaknesses. |
| 6. **Single-seed evidence.** The common seed was 42; multi-seed |
| uncertainty and confidence intervals are not established in this |
| card. |
| 7. **Fixed resolution.** The comparison uses 224 × 224 inputs. It does |
| not establish whether different resolutions would improve results. |
| 8. **Limited external reproducibility information.** Exact |
| hyperparameter exponents are not reliably preserved in the available |
| text extraction. Public release of trained weights and code is not |
| confirmed by the information summarized here. |
| 9. **Interpretability is not proof of correctness.** Grad-CAM and t-SNE |
| can support exploratory analysis but do not establish causal |
| reasoning or clinical validity. |
| 10. **Hardware-specific timing.** CPU latency is approximate and should |
| be benchmarked again on target hardware. |
| |
| ## 10. Ethical considerations and recommended safeguards |
|
|
| - Keep a qualified clinician responsible for interpretation of OCT |
| findings. |
| - Do not use model output as the sole basis for patient-care |
| decisions. |
| - Before any clinical research or deployment, conduct external |
| validation on patient-separated, institutionally diverse data and |
| report class-specific metrics and uncertainty. |
| - Audit duplicate images, patient overlap, label quality, and |
| acquisition-device distribution. |
| - Review false negatives and false positives by class, especially |
| Drusen. |
| - Define an abstention or human-review pathway for uncertain |
| predictions before any prospective evaluation. |
| - Monitor performance across relevant subgroups and acquisition |
| conditions where legally and ethically appropriate. |
| - Follow applicable data-protection, institutional-review, and |
| medical-device requirements. |
| |
| ## 11. Recommended model selection |
|
|
| For the reported experiment, **EfficientNet-B1 is the best-performing |
| choice by test accuracy (96.95%) and reported Drusen F1 (0.8780)**. |
| EfficientNet-B0 is smaller and had the lowest reported mean CPU latency |
| among the listed variants. EfficientNet-B7 achieved the highest reported |
| Drusen recall (0.9177), but it used 65.54 million total parameters and |
| had a reported mean CPU latency of approximately 1.25 seconds per image. |
|
|
| This is a study-specific recommendation, not a clinical recommendation. |
| The most appropriate variant depends on the target hardware and the |
| relative cost of class-specific errors; external validation is required |
| before selecting a model for real-world use. |
|
|
| ## 12. Reproducibility and release status |
|
|
| ----------------------------------------------------------------------- |
| Item Status based on the paper |
| ----------------------------------- ----------------------------------- |
| Dataset name Kermany four-class SD-OCT dataset |
|
|
| Input resolution 224 × 224 |
|
|
| Model family EfficientNet-B0 through B7 |
|
|
| Pretrained weights ImageNet |
|
|
| Framework TensorFlow 2.20.0 / Keras |
|
|
| Random seed 42 |
|
|
| Training hardware NVIDIA Tesla T4, 16 GB |
|
|
| Public source-code URL Not established in the paper |
| information summarized here |
| |
| Public trained-weight URL Not established in the paper |
| information summarized here |
| |
| External clinical validation Not established |
|
|
| Clinical approval / regulatory Not established; do not assume |
| status approval |
| ----------------------------------------------------------------------- |
|
|
| ## 13. Citation |
|
|
| Sherifi, Shkëlqim. *Within-Family EfficientNet Scaling under |
| Fixed-Resolution Transfer Learning for Spectral-Domain Optical Coherence |
| Tomography Retinal Disease Classification.* Manuscript submitted for |
| possible publication in **Diagnostics** (2026), as identified in the |
| supplied research paper. |
|
|
| ------------------------------------------------------------------------ |
|
|
| ### Model-card maintenance note |
|
|
| This card reflects the experimental protocol and results reported in the |
| supplied manuscript. Update it if the dataset split, preprocessing, |
| code, checkpoint, class order, training configuration, or evaluation |
| results change. Before publishing a checkpoint, record the exact model |
| variant, checkpoint checksum, software versions, preprocessing code, and |
| a reproducible inference example. |
