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auc-evaluation

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SuryaVamsi-P / Diabetic-Retinopathy-Detection-with-ResNet50

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Built an end-to-end deep learning pipeline using ResNet-50 to classify retinal images into five stages of Diabetic Retinopathy. Applied transfer learning, image preprocessing, and AUC-based evaluation on the APTOS 2019 Kaggle dataset, achieving a 94% validation AUC—offering real-world potential in clinical diagnosis automation.

computer-vision deep-learning tensorflow keras medical-imaging image-classification transfer-learning data-augmentation cnn-model clinical-decision-support multi-class-classification optical-imaging diabetic-retinopathy resnet50 early-detection medical-diagnosis healthcare-ai retinal-image-analysis aptos2019 auc-evaluation
  • Updated May 24, 2025
  • Python

tk-yasuno / multimodal-ad-lora-wallplug

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An innovative anomaly detection system for manufacturing industries. Integrating MVTec AD dataset, MiniCPM language model, and LoRA explanation generation, achieving AUC 0.7175 practical-level anomaly detection performance on 416 complete wallplugs dataset.

computer-vision industrial-automation lora unsupervised-learning anomaly-detection interpretable-ml explainable-ai quality-inspection defect-detection industrial-ai mvtec-ad explanation-generation minicpm auc-evaluation manufacturing-anomaly-detection wallplug-dataset domain-specific-ai
  • Updated Sep 28, 2025
  • Python

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