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Student seeks advice on improving inconsistent diabetic retinopathy AI model

A computer engineering student is seeking advice on improving a 5-class diabetic retinopathy detection model trained on the APTOS 2019 dataset. The model exhibits inconsistent predictions, misclassifying classes like Moderate as Severe or Proliferative, and shows high confidence even with incorrect predictions, especially on images outside the training dataset. The student has already tried various pretrained models, preprocessing techniques, and test-time augmentation, and is now considering ensemble models or investigating domain shift, class imbalance, or preprocessing issues. AI

IMPACT This query highlights common challenges in medical image classification, such as domain shift and class imbalance, which are critical for deploying reliable AI systems in healthcare.

RANK_REASON User is asking for advice on an existing model, not announcing a new release or research.

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Student seeks advice on improving inconsistent diabetic retinopathy AI model

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  1. r/MachineLearning TIER_1 English(EN) · /u/Delicious_Corner_754 ·

    How to improve a 5-class Diabetic Retinopathy model (APTOS 2019) – Mixed predictions across classes[P]

    <!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>I'm a final-year Computer Engineering student building a Flask-based AI Diabetic Retinopathy Detection system. The web application itself is complete with patient management, authentication, dashboard, PDF report generation, p…