IRO Journals
Journal of Innovative Image Processing

Morphological Transition Flow-based Feature Extraction for Five-Class Diabetic Retinopathy Classification

Open Access
Published: 01 July, 2026
Pages: 810-826
  • Basma Esserkassi , Basma Esserkassi
    ENSAM, E2SN Laboratory, Mohammed V University, Rabat, Morocco
    ENSAM, E2SN Laboratory, Mohammed V University, Rabat, Morocco
  • Souad Eddarouich , Souad Eddarouich
    Regional Educational Center, Rabat, Morocco
    Regional Educational Center, Rabat, Morocco
  • Abdennaser Bourouhou Abdennaser Bourouhou
    ENSAM, E2SN Laboratory, Mohammed V University, Rabat, Morocco
    ENSAM, E2SN Laboratory, Mohammed V University, Rabat, Morocco
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How to Cite

Esserkassi, Basma, Souad Eddarouich, and Abdennaser Bourouhou. 2026. “Morphological Transition Flow-Based Feature Extraction for Five-Class Diabetic Retinopathy Classification”. Journal of Innovative Image Processing 8 (3): 810-26. https://doi.org/10.36548/jiip.2026.3.004.
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Keywords

Diabetic Retinopathy
Random Forest
ELM
Bayesian Optimization
Clinical Deployment
EyePACS

Abstract

Diabetic retinopathy threatens the vision of 103 million people globally, yet computational and infrastructural barriers continue to block automated screening where it is needed most. We propose RF-MTF, a three-module framework evaluated on 34,860 EyePACS fundus images across five ICDR grades (validation set n=6,972). Module 1 applies AMS-CLAHE with Gabor morphological guidance. Module 2 introduces the Morphological Transition Flow — RGB channels processed by random projection networks (ELM, RVFL, BLS) with dual-scale morphological operations, yielding 384-dimensional feature vectors. Module 3 benchmarks three classifiers across 34 configurations spanning four optimization levels. Preprocessing variants produced near-identical F1-scores (0.9045–0.9086, Δ=0.41 pp) despite large image quality divergence, questioning the assumption that preprocessing optimization is critical for DR classification. The optimal configuration reached F1-weighted=0.9701, F1-macro=0.8910, AUC-ROC=0.9941. Random Forest achieved exceptional configuration-wise stability (σ=0.0002), in contrast to XGBoost's high optimization sensitivity (σ=0.2097) and SVM's persistent low performance on this feature space (σ=0.0108, F1≈0.25 across all configurations). Inference runs in 1–3 ms within a 20–40 MB footprint. RF-MTF delivers competitive 5-class ICDR classification on CPU hardware (F1-weighted=0.9701, F1-macro=0.8910), with minority-class performance (Grade 4 F1=0.78, 95% CI [0.70, 0.85], n=70) reflecting the inherent statistical challenges of evaluating rare classes under the natural 95.9:1 class imbalance of real-world DR screening populations — identified as the primary minority-class improvement target. External validation on Messidor-2, APTOS 2019, and IDRiD is the immediate next step.

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