Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data

Abstract

In this paper, we used the single-photon emission computerized tomography (SPECT) imaging technique to visualize the deficiency of dopamine-generated patterns inside the brain. These patterns are used to establish a patient’s disease progression, which helps distinguish the patients into different categories. Furthermore, we used a convolutional neural network (CNN) model to classify the patients based on the dopamine level inside the brain. The dataset used throughout this paper is the Parkinson’s progressive markers initiative (PPMI) dataset. The collected dataset was pre-processed and data amplification was performed to balance the imbalanced dataset. A CNN-based neural network was defined to classify input SPECT images into four categories. The motivation behind the proposed model is to reduce the number of resources consumed while maintaining the performance of the classification model. This will help the healthcare ecosystem run the classification model on mobile devices. The proposed model contains 14 layers with input layers, convolutional layers, max-pool layers, flatten layers, and dense layers with different dimensions. The dense layer classifies the patients into four different categories, including PSD, healthy control, scans without evidence of dopaminergic deficit (SWEDD), and GenReg PSD from the entire SPECT imaging dataset, which is used to establish the disease progression of different patients using SPECT images. The proposed model is trained with a large dataset with 58,692 images for training and 11,738 images for validation, and 7826 for testing. The proposed model outperforms the classification models from the surveyed papers. The proposed model’s accuracy is 0.889, recall is 0.9012, the precision is 0.9104, and the F1-score is 0.9057.

An Improved Dense CNN Architecture for Deepfake Image Detection

“…The model scored an average precision of 82.56% on these datasets, but the performance drastically drops to 65.18% on the validation dataset, as it is collected from various sources. Thus, a generalization through CNN on one dataset does not hold a cross-performance on another dataset [19]. The inconsistencies can be mitigated through an effective deep CNN (D-CNN) model that can address the cross-domain interpretability while maintaining the robustness and generalizability of the DF detection scheme, which would yield a high accuracy through an effective ensemble to the proposed CNN approaches.…”