Add scripts for patient classification and visualization

- Created `classification.py` for comparing image-level and patient-level classification results using various CNN models.
- Implemented `create_patient_groups.py` to extract features, generate PCA/t-SNE plots, and identify patient groups via K-means clustering.
- Added `figure6.py` to generate boxplots for test accuracy across multiple seeds.
- Developed `simple_patient_tsne.py` to perform t-SNE visualization of patient groups and save results in a manifest file.
- Introduced `simple_patient_manifest.csv` to store patient IDs, classes, image counts, and associated images.

scripts/classification.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+"""
+classification.py — single-seed image-level vs patient-level comparison.
+
+Usage:
+    conda activate fundus_imaging
+    python scripts/classification.py
+"""
+
+import os, sys, json
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from classes import PatientLeakageClassifier
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+MODELS = ["VGG16", "DenseNet121", "EfficientNetB1", "MobileNetV2", "ResNet50"]
+SEED = 20
+
+clf = PatientLeakageClassifier(
+    os.path.join(ROOT, "results", "simple_patient_manifest.csv"),
+    os.path.join(ROOT, "features"),
+    n_jobs=8)
+
+print(f"{'='*60}")
+print(f"IMAGE-LEVEL vs PATIENT-LEVEL (seed={SEED})")
+print(f"{'='*60}")
+
+results = []
+for name in MODELS:
+    print(f"\n  {name} ...")
+    img = clf.run(name, SEED, "image")
+    pat = clf.run(name, SEED, "patient")
+    results.append({"model": name,
+                    "image_cv": img["cv"], "image_test": img["test"],
+                    "patient_cv": pat["cv"], "patient_test": pat["test"],
+                    "drop": img["test"] - pat["test"]})
+    print(f"    Image:   CV={img['cv']:.4f}  Test={img['test']:.4f}")
+    print(f"    Patient: CV={pat['cv']:.4f}  Test={pat['test']:.4f}")
+    print(f"    Drop:    {img['test'] - pat['test']:.4f}")
+
+print(f"\n  {'Model':<18s} {'Img-CV':>8s} {'Img-Test':>9s} "
+      f"{'Pat-CV':>8s} {'Pat-Test':>9s} {'Drop':>7s}")
+print(f"  {'-'*54}")
+for r in results:
+    print(f"  {r['model']:<18s} {r['image_cv']:>8.4f} {r['image_test']:>9.4f} "
+          f"{r['patient_cv']:>8.4f} {r['patient_test']:>9.4f} {r['drop']:>7.4f}")
+
+with open(os.path.join(ROOT, "results", "classification_results.json"), "w") as f:
+    json.dump(results, f, indent=2)
+print(f"\nDONE")