Machine Learning-Based Classification of Retinitis Pigmentosa from Color Fundus Images: A Reproducible Benchmark and Screening-Oriented Pipeline.

Retinitis pigmentosa (RP) is a rare inherited retinal disorder in which fundus changes may be subtle and heterogeneous, limiting detection from color fundus images. This study evaluated multiple machine learning architectures for binary-RP versus healthy-control classification, and developed a reproducible pipeline for research-oriented screening support. Three publicly available fundus datasets were combined, including 248 RP images and 1045 healthy controls. An 80/20 train-test split was used, with targeted data augmentation applied only to RP images in the training set to address class imbalance. ConvNeXt-Tiny, ResNet101V2, EfficientNet-B0, a baseline classifier, and custom shallow convolutional neural networks were compared using accuracy, precision, recall, F1-score, confusion matrices, and ROC/precision-recall analyses. A compact ShallowCNN provided the best sensitivity-performance trade-off. On the fixed image-level test set, Adam with a learning rate of 0.0005 reached 96.51% accuracy, while SGD with a learning rate of 0.001 achieved 98% RP recall, minimizing false negatives. The trained models were exported to ONNX and integrated into a Windows inference tool. The proposed framework provides an open, reproducible benchmark for technical evaluation, although external validation is required before clinical use.