Comparison of normalized confusion matrices for multiclass DR classification on the APTOS and Messidor-2 datasets using different preprocessing methods: Our pipeline with No Preprocessing (left), CLAHE preprocessing (middle), and Ben-Graham preprocessing (right).

مواد مشابهة

• Precision-recall curves for multiclass DR classification on the APTOS and Messidor-2 datasets using three different preprocessing methods: our pipeline with no preprocessing (left), CLAHE Preprocessing (middle), and Ben-Graham preprocessing (right).
  حسب: Zarin Akter (22794652)
  منشور في: (2025)
• Comparison with SOTA: After training the models using three different approaches—(1) applying CLAHE (), (2) applying Ben Graham’s preprocessing technique (♦), and (3) our proposed () pipeline without sophisticated preprocessing—we evaluated them on the actual test sets of the APTOS2019 and Messidor-2 datasets.
  حسب: Zarin Akter (22794652)
  منشور في: (2025)
• Model architecture: The dataset undergoes basic preprocessing (e.g., resizing, transformations, rotations) to prepare the data.
  حسب: Zarin Akter (22794652)
  منشور في: (2025)
• Traditional DR classification (up) relies on CNN-based models with extensive preprocessing on raw data [15,16].
  حسب: Zarin Akter (22794652)
  منشور في: (2025)
• External validation on EyePACS: Performance of the two best backbones from our pipeline—DenseNet-169 (initially optimized on Messidor-2) and MobileViT (initially optimized on APTOS2019)—after a brief fine-tuning stage on the EyePACS dataset.
  حسب: Zarin Akter (22794652)
  منشور في: (2025)