Three Dimensional Analysis of SPECT Images for Diagnosing Early Parkinson’s Disease using Radial Basis Function Kernel − Extreme Learning Machine

Abstract

Background: Parkinson’s Disease (PD) is caused by the deficiency of dopamine, the neurotransmitter that has an effect on specific uptake region of the substantia nigra. Identification of PD is quite tough at an early stage. Objective: The present work proposes an expert system for three dimensional Single-Photon Emission Computed Tomography (SPECT) image to diagnose the early PD. Methods: The transaxial image slices are selected on the basis of their high specific uptake region. The processing techniques like preprocessing, segmentation and feature extraction are implemented to extract the quantification parameters like Intensity, correlation, entropy, skewness and kurtosis of the images. The Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers using Radial Basis Function kernel (RBF) are implemented and their results are compared in order to achieve better performance of the system. The performance of the system is evaluated in terms of sensitivity, specificity analysis, accuracy, Receiver Operating Curve (ROC) and Area Under the Curve (AUC). Results: It is found that RBF-ELM provides high accuracy of 98.2% in diagnosing early PD. In addition, the similarity among the features is found out using K-means clustering algorithm to compute the threshold level for early PD. The computed threshold level is validated using Analysis of Variance (ANOVA). Conclusion: The proposed system has a great potential to assist the clinicians in the early diagnosis process of PD.

Identification of Key Contributive Compounds in a Herbal Medicine: A Novel Mathematic—Biological Evaluation Approach

“…However, non‐linearly separable problems can generate in both the GRA and MLRA calculation [22] . Notably, radial basis function analysis (RBFA) is capable of transforming samples values into a high‐dimensional descriptor space and then linearly calculated the contributive degree in a low dimensional space by a curve fitting method, also known as the artificial neural network [23] . Since the non‐linearly separable problem can be solvable by processing data in a high‐dimensional computing environment.…”

Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review

“…Recently, the same group has also worked on using NN approach to detect PD directly from DAT-SPECT projection data (38). Differing from other methods where reconstructed images were used (24)(25)(26)(27)(28)(29)(30)(31)(32)(33), in this method the NN accepted SPECT projection data and a conclusion was made from the analysis in the projection-data space directly. The hypothesis was that all the necessary information for accurate diagnosis was already presented in the projection-data, and reconstruction into images was simply to allow easy interpretation by a human observer.…”