---
license: apache-2.0
library_name: pytorch
tags:
- medical-imaging
- ophthalmology
- fundus
- image-classification
- retinal-disease
- benchmark
- ensemble
- pytorch
- cross-validation
datasets:
- DoB24/fundus-10class-augmented
metrics:
- accuracy
- f1
- roc-auc
- cohen-kappa
- brier-score
- expected-calibration-error
pipeline_tag: image-classification
---

# Fundus Lesion Image Classification — 9-Model Comparative Benchmark

> **Companion artifact for the Master's thesis _"Classification of Fundus
> Lesion Images Using Deep Learning Models"_ (Xidian University, 2026), by
> Daryl Panashe Katiyo.**
>
> Reproducible weights, predictions, and full statistical analysis for nine
> deep-learning backbones evaluated on a 10-class colour-fundus dataset
> with a group-aware (perceptual-hash) 5-fold cross-validation protocol.

---

## Important Note on Tables

> **Two result tables appear in this README. They measure different things
> and must not be mixed:**
>
> | Table | Protocol | # runs | Use for |
> |-------|----------|--------|---------|
> | **§ 5.1 — 5-fold CV (authoritative)** | 5 independent train/val splits, fixed holdout test | 5 | Paper, model comparison, all citations |
> | **§ 5.2 — Single-run baseline** | One training run, fold-0 split | 1 | Legacy reference, documents CLIP variance |
>
> **Always cite § 5.1 in the paper.** The single-run baseline (§ 5.2) is
> retained for completeness — CLIP underperforms there (86.25%) due to a
> single unlucky initialisation versus 90.15% over 5 folds.

---

## 1. Abstract

Automatic interpretation of colour fundus photographs is a foundational
task for screening prevalent blinding diseases such as diabetic
retinopathy, glaucoma and age-related macular degeneration. We
benchmark **nine deep-learning backbones** spanning four architectural
families — classical CNNs (VGG-19, ResNet-50, ResNet-101, DenseNet-121,
Inception-v3), vision-language pretraining (OpenAI CLIP ViT-B/16),
self-supervised vision transformers (DINOv2-L/14), hierarchical
transformers (Swin-B), and a domain-specific MAE pretraining
(RETFound MAE ViT-L/16) — on a 10-class fundus dataset of 16 242
augmented images. To suppress augmentation-induced label leakage we
construct a **group-aware (perceptual-hash) stratified 5-fold split**
and report mean accuracy, F1, ROC-AUC, ECE, Cohen's κ, Brier score,
bootstrap 95% confidence intervals, Bonferroni-corrected McNemar tests,
and 90% Mondrian conformal sets.

**Headline result (5-fold CV).** Classical CNNs and CLIP ViT-B/16 are
statistically indistinguishable at the top, with Inception-v3 leading
at **90.18% accuracy** (F1 = 92.54%, κ = 0.884, ROC-AUC = 0.9930).
Contrary to expectations, foundation models pretrained on general vision
(DINOv2-L: 89.61%, Swin-B: 87.00%) or fundus images (RETFound: 83.35%)
do not outperform classical CNNs on this moderate-sized dataset.
A soft-vote ensemble of all nine models reaches **ROC-AUC = 0.9941**
and accuracy = 89.68%.

---

## 2. Motivation & Model Selection

Modern fundus screening pipelines are increasingly built on pre-trained
image backbones, but the question _"which backbone family is best for
fundus disease classification on a moderately-sized, imbalanced
dataset?"_ has no consensus answer. We deliberately chose backbones
that exercise four distinct **inductive biases / pretraining regimes**:

| Family | Backbone(s) | Why included |
|--------|-------------|--------------|
| Classical CNNs | VGG-19, ResNet-50, ResNet-101, DenseNet-121, Inception-v3 | Established baselines used in virtually all prior fundus benchmarks ([Gulshan 2016][1], [Ting 2017][2]). Locally-connected convolutions suit texture-dominant retinal pathology. |
| Vision-language (CLIP) | OpenAI CLIP ViT-B/16 | Tests whether 400 M-pair web-scale contrastive pretraining transfers to a tightly-constrained medical domain. |
| Self-supervised ViT | DINOv2-L/14 | State-of-the-art general-purpose features without language supervision ([Oquab 2024][3]). |
| Hierarchical ViT | Swin-B | Adds hierarchy + shifted windows; competitive on ImageNet at lower compute than ViT-L ([Liu 2021][4]). |
| Domain MAE | RETFound MAE ViT-L/16 | Pretrained on **1.6 M colour fundus images** ([Zhou 2023, Nature][5]); the strongest published prior on this modality. |

This grid isolates three confounders: (i) **scale** (ResNet-50 vs
ResNet-101; ViT-B vs ViT-L); (ii) **pretraining modality** (ImageNet
supervised vs CLIP language-supervised vs DINOv2 self-supervised vs
RETFound domain-MAE); and (iii) **architecture class** (CNN vs ViT vs
hierarchical).

---

## 3. Dataset

- **Source.** [Mendeley Data][6] (10 classes; 5 335 original images).
- **Augmentation.** Class-balancing augmentation expanded the pool to
  16 242 images (rotation, horizontal flip, brightness/contrast jitter,
  Gaussian blur). Each augmented image carries its source's diagnostic label.
- **Companion dataset:** [DoB24/fundus-10class-augmented](https://huggingface.co/datasets/DoB24/fundus-10class-augmented).

### 3.1 Class distribution

| Index | Class | Original | Augmented |
|-------|-------|----------|-----------|
| 0 | Central Serous Chorioretinopathy | 101 | 606 |
| 1 | Diabetic Retinopathy | 1 509 | 3 444 |
| 2 | Disc Edema | 127 | 762 |
| 3 | Glaucoma | 1 349 | 2 880 |
| 4 | Healthy | 1 024 | 2 676 |
| 5 | Macular Scar | 444 | 1 937 |
| 6 | Myopia | 500 | 2 251 |
| 7 | Pterygium | 17 | 102 |
| 8 | Retinal Detachment | 125 | 750 |
| 9 | Retinitis Pigmentosa | 139 | 834 |
| — | **Total** | **5 335** | **16 242** |

### 3.2 Group-aware splitting (data-leakage prevention)

Because the augmented set contains visually near-duplicate copies of
each original image, a naïve `train_test_split` over the augmented
pool would let models memorise patient-level identities. We prevent
this by:

1. Computing a 64-bit perceptual hash (`pHash`) on every image.
2. Linking each augmented image to its nearest original at Hamming
   distance ≤ 8 → defines a `group_id`.
3. Running scikit-learn `StratifiedGroupKFold(n_splits=5)` so that
   **all augmented children of a given original sit in exactly one fold**.

The held-out **test set is fixed across all 5 folds**: 3 208 images
(≈ 19.8% of the augmented pool). Exact manifest:
`splits/holdout_split_augmented.json` (3.2 MB).

---

## 4. Training Protocol

### 4.1 CNN / CLIP backbones

| Hyper-parameter | Value |
|-----------------|-------|
| Optimizer | AdamW (β₁=0.9, β₂=0.999, weight-decay=1×10⁻⁴) |
| Initial LR | 2×10⁻⁴ |
| LR schedule | 3-epoch linear warm-up + cosine decay to 0 |
| Epochs | Up to 60 (early stop patience=12 on val F1) |
| Batch size | 32 |
| Image size | 224×224 (Inception-v3: 299×299) |
| Preprocessing | CLAHE (LAB L-channel) → RandAugment (n=2, m=9) → ImageNet normalisation |
| Imbalance | `WeightedRandomSampler` (weights ∝ 1/class_count) |
| Regularisation | MixUp (α=0.2) + CutMix (α=1.0, p=0.7) |
| Mixed precision | `torch.amp.autocast` + `GradScaler` |
| Test-time aug | 6 views (centre + 4 corners + h-flip), soft-vote mean |
| Hardware | PyTorch 2.11 + CUDA 12.8, NVIDIA Tesla T4 (16 GB) |

### 4.2 Foundation model backbones (DINOv2-L, Swin-B, RETFound)

Two-stage schedule per fold:

| Stage | Layers trained | Epochs | Head LR | Backbone LR |
|-------|----------------|--------|---------|-------------|
| Linear probe | Head only | 20 | 1×10⁻³ | frozen |
| Full fine-tune | All layers | 15 | 1×10⁻⁴ | 1×10⁻⁵ |

Batch size: 24. Early stopping patience: 8 epochs on val F1.

### 4.3 Ensemble

F1-weighted soft-vote across all 9 models, using each model's
validation F1 as the weight.

---

## 5. Results

All metrics are on the **fixed 3 208-image holdout test set**.

---

### 5.1 Five-fold cross-validation — **authoritative results (use in paper)**

> Acc, F1, 95% CI, ROC-AUC, and ECE are **means over 5 independent
> training runs**. κ and Brier for CNN/CLIP are from 5-fold pooled
> predictions. For foundation models (†), κ and Brier are from
> single-run inference on the same holdout (fold-level predictions
> not stored); acc/F1/ROC/ECE are still 5-fold averages.

| Rank | Model | Acc (%) | 95% CI | F1 (%) | F1 95% CI | κ | Brier | ROC-AUC | ECE |
|------|-------|---------|--------|--------|-----------|---|-------|---------|-----|
| 1 | `inception_v3` | **90.18** | [89.24, 91.24] | 92.54 | [91.68, 93.40] | 0.884 | 0.150 | 0.9930 | 0.0194 |
| 2 | `clip_openai` | **90.15** | [89.18, 91.24] | **92.83** | [92.00, 93.61] | 0.884 | 0.140 | **0.9944** | 0.0217 |
| 3 | `vgg19` | **90.12** | [89.09, 91.12] | 92.59 | [91.77, 93.41] | 0.884 | 0.150 | 0.9933 | 0.0228 |
| 4 | `resnet101` | **90.09** | [89.15, 91.12] | 92.63 | [91.77, 93.47] | 0.883 | 0.140 | 0.9941 | 0.0243 |
| 5 | `densenet121` | 89.65 | [88.62, 90.71] | 92.29 | [91.37, 93.07] | 0.878 | 0.150 | 0.9937 | 0.0272 |
| 6 | `dinov2_l` | 89.61 | [88.57, 90.64] | 92.27 | [91.38, 93.08] | 0.876† | 0.160† | 0.9934 | 0.0299 |
| 7 | `resnet50` | 89.50 | [88.40, 90.59] | 92.20 | [91.34, 93.03] | 0.876 | 0.140 | **0.9945** | 0.0339 |
| 8 | `swin_b` | 87.00 | [85.92, 88.15] | 90.26 | [89.31, 91.19] | 0.845† | 0.190† | 0.9896 | 0.0294 |
| 9 | `retfound` | 83.35 | [82.16, 84.65] | 87.27 | [86.22, 88.35] | 0.810† | 0.240† | 0.9834 | 0.0242 |
| — | **9-Model Ensemble** | **89.68** | [88.65, 90.74] | **92.25** | — | 0.878 | 0.144 | **0.9941** | 0.0198 |

† κ and Brier for DINOv2-L, Swin-B, RETFound from single-run holdout
inference. All other foundation model metrics are 5-fold CV averages.

**Key findings:**
- Top-4 models (Inception-v3, CLIP, VGG-19, ResNet-101) are
  statistically indistinguishable: all 6 pairwise McNemar tests p > 0.05
  after Bonferroni correction (see `kfold/cnn_clip/mcnemar.json`).
- DINOv2-L (89.61%) matches DenseNet-121 (89.65%) within noise despite
  having 7× more parameters.
- RETFound, pretrained on 1.6 M fundus images, ranks last — the
  LP+FT protocol with patience=8 may be insufficient on this dataset size.
- The ensemble gains ROC-AUC parity with the best individual model.

Machine-readable full table: `kfold/kfold_v2_summary.csv`

---

### 5.2 Single-run baseline (§ 5.2 — for reference only, do not cite in paper)

> Retained to document the original preliminary experiment (one run,
> fold-0 split). CLIP's 86.25% here vs 90.15% in § 5.1 is single-run
> variance, not an architectural effect.

| Rank | Model | Acc (%) | 95% CI | F1 (%) | κ | Brier | ROC-AUC |
|------|-------|---------|--------|--------|---|-------|---------|
| 1 | `densenet121` | 89.78 | [88.71, 90.80] | 92.26 | 0.879 | 0.148 | 0.9931 |
| 2 | `dinov2_l` | 89.50 | [88.47, 90.55] | 92.15 | 0.876 | 0.155 | 0.9938 |
| 3 | `vgg19` | 89.31 | [88.22, 90.40] | 92.12 | 0.874 | 0.154 | 0.9930 |
| 4 | `resnet101` | 89.25 | [88.19, 90.34] | 92.05 | 0.873 | 0.149 | 0.9941 |
| 5 | `inception_v3` | 89.21 | [88.15, 90.28] | 91.97 | 0.873 | 0.157 | 0.9934 |
| 6 | `resnet50` | 89.09 | [88.00, 90.12] | 91.87 | 0.871 | 0.147 | 0.9944 |
| 7 | `swin_b` | 86.85 | [85.69, 88.03] | 90.44 | 0.845 | 0.185 | 0.9904 |
| 8 | `clip_openai` | 86.25 | [85.10, 87.41] | 89.99 | 0.838 | 0.195 | 0.9896 |
| 9 | `retfound` | 83.88 | [82.64, 85.10] | 87.68 | 0.810 | 0.238 | 0.9838 |
| — | **9-Model Ensemble** | **89.68** | [88.65, 90.74] | **92.25** | 0.878 | 0.144 | **0.9941** |

> The ensemble row is identical in both tables — it is computed once on
> the fixed holdout and does not depend on the training run.

---

### 5.3 Statistical significance

All 15 pairwise Bonferroni-corrected McNemar tests among the 6 CNN/CLIP
models: **p > 0.05** — no statistically significant pairwise differences.
Full χ² and p-value matrix: `kfold/cnn_clip/mcnemar.json`.

---

### 5.4 Calibration (ECE)

| Model | ECE | |
|-------|-----|-|
| `inception_v3` | **0.0194** | Best calibrated |
| `clip_openai` | 0.0217 | |
| `vgg19` | 0.0228 | |
| `retfound` | 0.0242 | |
| `resnet101` | 0.0243 | |
| `densenet121` | 0.0272 | |
| `swin_b` | 0.0294 | |
| `dinov2_l` | 0.0299 | |
| `resnet50` | 0.0339 | |
| **9-Model Ensemble** | **0.0198** | Best overall |

Reliability diagrams: `analysis/reliability_diagrams/reliability_<model>.png`.

---

## 6. Reproducibility

### 6.1 Load a model (PyTorch ≥ 2.6)

```python
import torch, timm
from huggingface_hub import hf_hub_download

# CNN backbone — any of: inception_v3, densenet121, vgg19, resnet101, resnet50
ckpt = hf_hub_download("DoB24/fundus-9model-benchmark",
                       "weights/inception_v3_v2_final.pth")
model = timm.create_model("inception_v3", num_classes=10)
state = torch.load(ckpt, map_location="cpu", weights_only=False)
model.load_state_dict(state["model"] if "model" in state else state)
model.eval()
```

```python
# CLIP ViT-B/16
import open_clip, torch
from huggingface_hub import hf_hub_download

ckpt = hf_hub_download("DoB24/fundus-9model-benchmark",
                       "weights/clip_openai_v2_final.pth")
model, _, preprocess = open_clip.create_model_and_transforms(
    "ViT-B-16", pretrained="openai")
state = torch.load(ckpt, map_location="cpu", weights_only=False)
model.load_state_dict(state["model"] if "model" in state else state)
model.eval()
```

### 6.2 Inference preprocessing

```python
from torchvision import transforms
import cv2
from PIL import Image

def clahe_preprocess(img_path):
    img = cv2.imread(str(img_path))
    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
    l, a, b = cv2.split(lab)
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    lab = cv2.merge([clahe.apply(l), a, b])
    img = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
    return Image.fromarray(img)

val_transform = transforms.Compose([
    transforms.Resize((224, 224)),   # use 299 for inception_v3
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406],
                         [0.229, 0.224, 0.225]),
])
```

### 6.3 Class index mapping

```
0: Central Serous Chorioretinopathy [Color Fundus]
1: Diabetic Retinopathy
2: Disc Edema
3: Glaucoma
4: Healthy
5: Macular Scar
6: Myopia
7: Pterygium
8: Retinal Detachment
9: Retinitis Pigmentosa
```

### 6.4 Quick inference

```bash
pip install torch torchvision timm open_clip_torch huggingface_hub pillow opencv-python
python code/inference_example.py path/to/fundus.jpg --model inception_v3
```

---

## 7. Files in this repository

| Path | Description |
|------|-------------|
| `weights/<model>_v2_final.pth` ×9 | Fine-tuned weights — dict with keys `model`, `optimizer`, `epoch` |
| `results/<model>_test.json` ×9 | Single-run holdout metrics (acc, F1, κ, Brier, ROC-AUC, per-class) |
| `results/<model>_test_preds.json` ×9 | Single-run labels + preds + probs (3 208 items) |
| `results/ensemble_report.json` | Ensemble + McNemar + conformal report (single-run) |
| `kfold/kfold_v2_summary.csv` | **Authoritative 9-model 5-fold summary (machine-readable)** |
| `kfold/cnn_clip/summary.json` | 5-fold aggregated means + CI for CNN/CLIP models |
| `kfold/cnn_clip/<model>_kfold.json` ×6 | Per-fold val metrics for CNN/CLIP |
| `kfold/cnn_clip/<model>_test_preds.json` ×6 | 5-fold pooled predictions |
| `kfold/cnn_clip/mcnemar.json` | 15 pairwise McNemar tests |
| `kfold/foundation_fold{0-4}_{model}.json` ×15 | Per-fold test metrics for foundation models |
| `splits/holdout_split_augmented.json` | pHash-grouped 5-fold manifest (3.2 MB) |
| `analysis/confusion_matrices/cm_<model>.png` ×10 | Per-model confusion matrices |
| `analysis/roc_curves/roc_<model>.png` ×10 | One-vs-rest ROC curves |
| `analysis/reliability_diagrams/reliability_<model>.png` ×10 | Calibration reliability diagrams |
| `analysis/per_class_metrics.csv` | Precision / recall / F1 / support per model per class |
| `analysis/ece_summary.json` | ECE values all models |
| `gradcam/gradcam_<model>.png` ×9 | GradCAM / input-gradient saliency maps |
| `code/hparams.json` | Full hyperparameter table |
| `CITATION.cff` | Citation File Format |

---

## 8. Compute Disclosure

All 9 models trained across 5 folds on a single **NVIDIA Tesla T4 (16 GB)**,
PyTorch 2.11.0+cu128.

| Model | Approx. GPU-hours (5 folds total) |
|-------|----------------------------------|
| VGG-19 | 12.5 |
| ResNet-50 | 11.5 |
| ResNet-101 | 15.5 |
| DenseNet-121 | 14.0 |
| Inception-v3 | 11.0 |
| CLIP ViT-B/16 | 19.0 |
| DINOv2-L | 56.0 |
| Swin-B | 22.5 |
| RETFound | 47.0 |
| Ensemble + stats | 0.3 |
| **Total** | **~209 GPU-hours** |

---

## 9. Citation

```bibtex
@mastersthesis{katiyo2026fundus,
  author  = {Katiyo, Daryl Panashe},
  title   = {Classification of Fundus Lesion Images Using Deep Learning Models},
  school  = {Xidian University},
  year    = {2026},
  note    = {Companion artifact: \url{https://huggingface.co/DoB24/fundus-9model-benchmark}}
}
```

```bibtex
@dataset{nayan2023fundus,
  author  = {Nayan, Asma U. and Saha, Sajib K. et al.},
  title   = {A Curated Dataset of Retinal Fundus Images for Disease Classification},
  year    = {2023},
  doi     = {10.17632/s9bfhswzjb.1},
  url     = {https://data.mendeley.com/datasets/s9bfhswzjb/1}
}
```

---

## 10. References

[1]: https://doi.org/10.1001/jama.2016.17216
[2]: https://doi.org/10.1001/jama.2017.18152
[3]: https://arxiv.org/abs/2304.07193
[4]: https://arxiv.org/abs/2103.14030
[5]: https://www.nature.com/articles/s41586-023-06555-x
[6]: https://data.mendeley.com/datasets/s9bfhswzjb/1

1. **Gulshan V. et al.** "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs." *JAMA* 316.22 (2016): 2402–2410.
2. **Ting D.S.W. et al.** "Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases." *JAMA* 318.22 (2017): 2211–2223.
3. **Oquab M. et al.** "DINOv2: Learning Robust Visual Features without Supervision." arXiv:2304.07193 (2023).
4. **Liu Z. et al.** "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows." *ICCV* 2021.
5. **Zhou Y. et al.** "A foundation model for generalizable disease detection from retinal images." *Nature* 622 (2023): 156–163.
6. **He K. et al.** "Deep Residual Learning for Image Recognition." *CVPR* 2016.
7. **Simonyan K., Zisserman A.** "Very Deep Convolutional Networks for Large-Scale Image Recognition." *ICLR* 2015.
8. **Huang G. et al.** "Densely Connected Convolutional Networks." *CVPR* 2017.
9. **Szegedy C. et al.** "Rethinking the Inception Architecture for Computer Vision." *CVPR* 2016.
10. **Radford A. et al.** "Learning Transferable Visual Models From Natural Language Supervision." *ICML* 2021.
11. **Zhang H. et al.** "mixup: Beyond Empirical Risk Minimization." *ICLR* 2018.
12. **Yun S. et al.** "CutMix: Regularization Strategy to Train Strong Classifiers." *ICCV* 2019.
13. **Cubuk E.D. et al.** "RandAugment: Practical Automated Data Augmentation." *NeurIPS* 2020.
14. **Vovk V., Gammerman A., Shafer G.** *Algorithmic Learning in a Random World.* Springer, 2005.
15. **Bonferroni C.E.** "Teoria statistica delle classi e calcolo delle probabilità." 1936.

---

## 11. License & Contact

Apache-2.0 for code and weights. Original Mendeley dataset: CC BY 4.0.

Questions / collaboration: open an Issue on the Hub repo.