An Explainable Approach for Early Parkinson Disease Detection Using Deep Learning

Parkinson’s disease (PD) is a progressive disorder that affects the nervous system and all the parts of the body controlled by it. It is the second most diffused neurodegenerative disorder, showing increasing trends in the last years and requiring new tools and procedures for diagnosis and assessment. In order to be used in medical clinics, the PD detection approaches require high effectiveness in disease detection and good capability to drive the experts in the comprehension and checking of the prediction’s reasons. According to this, this paper proposes an explainable Deep Learning approach for the detection of PD from single photon emission computed tomography (SPECT) images. The approach consists of a combination of a CNN prediction model and a Gradient weighted Class Activation Mapping (Grad-CAM) interpretable technique. The validation is performed on a known dataset belonging to Parkinson’s Progression Markers Initiative (PPMI). For this dataset, SPECT images of 974 patients are used showing good accuracy in the classification of healthy and PD patients and a good capability to explain the obtained prediction.