Parkinson’s disease (PD), a prevalent neurodegenerative disorder characterized by striataldopamine deficiency, poses challenges in early and accurate diagnosis, often leading tomisclassification with atypical parkinsonism. While conventional imaging techniques like
PET andSPECT detect changes in striatal dopamine levels and functional changes and correlate them with motorresponses, they necessitate expert interpretation. This study addresses these diagnostic complexities byleveraging machine learning (ML) and convolutional neural networks (CNN), to automate and expeditePD accurate diagnostics. Our research focuses on analyzing ۴۰۰
PET images from visits at months ۱۲,۳۶, and ۴۸ from the Parkinson’s Progression Markers Initiative (PPMI) dataset. The goal is to identifysimilarities between brain images of PD patients for enhanced diagnostic accuracy. In the first step, weresize our ۳D images (۱۲۸x۱۲۸x۱۲۸) to ۲D images (۱۲۸x۱۲۸) by applying average pooling to reducecomplexity. Subsequently, we further resize these images to ۲D (۶۴x۶۴). Following the preprocessingsteps, we employ a convolutional autoencoder to extract similar features, thereby enhancing theclustering operation. After clustering using MeanShift, we analyze each cluster for every pair of originalimages. For each pair, we calculate the correlation matrix and average all correlation matrices to obtaina single correlation matrix. In the last step, we scrutinize the final correlation matrix to identify cellswith values closer to one. We map these cells to the corresponding pixels, and by aggregating highcorrelationpixels, we identify the regions that are repeated in our images. Employing advanced AIalgorithms, our methodology trains models to recognize recurring similar regions across images. Thisenables the detection and computation of specific brain regions, potentially indicative of PD. Automatedand rapid diagnosis by integrating ML and CNN into
PET imaging analysis has the potential to enhancepersonalized treatment strategies and assist doctors in facilitating the diagnostic process.