Revisiting CNN-Based Parkinson’s Disease Classification from DaT-SPECT Images: The Role of Training Protocols

Parkinson’s disease (PD) is a progressive neurodegenerative disorder for which dopamine transporter single-photon emission computed tomography (DaT-SPECT) is widely used to support clinical diagnosis. Recent convolutional neural network (CNN)-based studies have reported high classification accuracy on DaT-SPECT datasets. However, the relative contributions of network architecture and training protocol design to these results remain insufficiently explored, particularly for small and moderately sized medical imaging datasets. In this study, a training-oriented evaluation of CNNs for PD classification is conducted using two DaT-SPECT datasets derived from the Parkinson’s Progression Markers Initiative (PPMI). First, a previously published experimental setup is faithfully reproduced on a curated dataset of 645 DaT-SPECT images using identical preprocessing procedures, data splits, and model architectures. Under the reproduced experimental setting, previously reported classification accuracies for individual CNN architectures ranged from 93.02% to 95.34%, while an ensemble approach achieved 98.45% accuracy. The same architectures are then evaluated using a unified training protocol incorporating standardized optimization and regularization strategies. Using this protocol, ResNet50 achieves 100% classification accuracy, with all evaluation metrics reaching 1.0, while VGG16, Inception V3, and Xception each achieve an accuracy of 99.22%. On a larger, independently constructed PPMI-derived dataset with higher spatial resolution, previously reported classification accuracies ranged from 93.27% for PD vs. SWEDD to 95.33% for PD vs. control. Using the proposed unified training protocol, the evaluated CNN architectures achieve classification accuracies of 98% for PD vs. SWEDD and 100% for PD vs. control. These results indicate that training protocol design has a stronger influence on DaT-SPECT-based PD classification performance than the specific choice of CNN architecture.