← Back to all news
arXiv AI By Shkelqim Sherifi

Within-Family EfficientNet Scaling under Fixed-Resolution Transfer Learning for Spectral-Domain Optical Coherence Tomography Retinal Disease Classification

Read the original on arXiv AI →

The study evaluates EfficientNet variants B0–B7 for classifying retinal diseases from spectral-domain optical coherence tomography (SD‑OCT) images at a fixed 224×224 resolution. Eight ImageNet‑pretrained models were tested on the Kermany dataset, with training involving a frozen‑backbone head followed by partial fine‑tuning. Results show non‑monotonic accuracy, with B1 achieving the highest overall accuracy (96.95%) and best Drusen F1, while B7 offered a secondary accuracy gain at the cost of larger model size and CPU latency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Oct 16

International Multi-Centre Validation of Unsupervised Domain Adaptation for Precise Discrimination between Normal and Abnormal Retinal Development

The study presents an unsupervised domain adaptation model that classifies normal versus abnormal retinal development using optical coherence tomography (OCT) scans from multiple devices and centres. Trained on labelled data from one OCT system and unlabelled data from another, the ResNet50‑based Domain Adversarial Neural Network achieved accuracies between 87.75% and 96.06% across six source‑to‑target pairings and maintained performance on an unseen Heidelberg Spectralis dataset. The approach demonstrates that OCT classification can be transferred across devices without requiring labelled target data, potentially enabling more consistent assessment of retinal development and large‑scale analysis of existing imaging datasets.

By Zhanhan Tu, Nikhil Reddy, Helen J. Kuht, Ha-Jun Yoon, Gail D. E. Maconachie, Ravi Purohit, Prateek Pani, Line Kessel, Jinu Han, Jing Jin, Shui-Hua Wang, Karthik Bharadwaj, Viral Sheth, Michael Hisaund, Rebecca J. McLean, George Garratt, Chetan Kantibhai Patel, Kanmin Xue, Callum Hunt, Zisen Zhou, Ian De Silva, Seema Teli, Sonal Shah, Vanessa Rodwell, Aarti Patel, Irene Gottlob, Yu-Dong Zhang, Zedong Zheng, Girish Varma, Mervyn G. Thomas
fine-tuningsafety
More like this →
arXiv Computer Vision
Sep 3

Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detection

The study evaluates fundus-specific foundation models (FM) for detecting diabetic macular edema (DME) in retinal images. It compares two popular FM—RETFound and FLAIR—against a lightweight EfficientNet-B0 backbone across multiple datasets (IDRiD, MESSIDOR-2, and OCT-and-Eye-FundusImages). Results indicate that FM do not consistently outperform fine‑tuned CNNs; EfficientNet-B0 often matches or exceeds FM performance, with FLAIR being the most competitive FM.

By Franco Javier Arellano, Jos\'e Ignacio Orlando
fine-tuning
More like this →
arXiv Computer Vision
Oct 19

Beyond Multi-Stage Pipelines: A Label-Correlation Ensemble for Multi-Label Retinal Fundus Disease Classification

The paper evaluates three architectures for multi-label retinal disease classification on a unified 39‑target dataset. A staged gated cascade, a flat sigmoid model, and a label‑correlation transformer are compared, all sharing the same backbone and training protocol. The label‑correlation model yields the largest performance gain, and blending it with the flat model under test‑time augmentation achieves the best results across most metrics.

By Md Asif Uddin, Jannatul Ferdous, Asif Islam, Zobaer Mahmud Zisan, Promit Ghosh, Shah Ahafik Arman, Niloy Farhan, Rafeed Rahman
llms
More like this →