Soft Attention Based DenseNet Model for Parkinson’s Disease Classification Using SPECT Images

Abstract

ObjectiveDeep learning algorithms have long been involved in the diagnosis of severe neurological disorders that interfere with patients’ everyday tasks, such as Parkinson’s disease (PD). The most effective imaging modality for detecting the condition is DaTscan, a variety of single-photon emission computerized tomography (SPECT) imaging method. The goal is to create a convolutional neural network that can specifically identify the region of interest following feature extraction.MethodsThe study comprised a total of 1,390 DaTscan imaging groups with PD and normal classes. The architecture of DenseNet-121 is leveraged with a soft-attention block added before the final classification layer. For visually analyzing the region of interest (ROI) from the images after classification, Soft Attention Maps and feature map representation are used.OutcomesThe model obtains an overall accuracy of 99.2% and AUC-ROC score 99%. A sensitivity of 99.2%, specificity of 99.4% and f1-score of 99.1% is achieved that surpasses all prior research findings. Soft-attention map and feature map representation aid in highlighting the ROI, with a specific attention on the putamen and caudate regions.ConclusionWith the deep learning framework adopted, DaTscan images reveal the putamen and caudate areas of the brain, which aid in the distinguishing of normal and PD cohorts with high accuracy and sensitivity.

An Improved Method for Diagnosis of Parkinson’s Disease using Deep Learning Models Enhanced with Metaheuristic Algorithm

“…VGG16 (Visual Geometry Group 16) [ 11 ]is a deep CNN architecture that has suggested by the University of Oxford's Visual Geometry Group in 2014.It is created for image classification problems and has accomplished state-of-the-art performance on various benchmarks, including the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset. Thirteen Conv (convolutional) layers, 3 fully connected dense layers, and other layers made up the 16-layer,VGG16.…”

“…Also, the authors suggest a mechanism for evaluating interpretation performance as well as a way to use interpreted input to aid in model selection. [ 11 ] suggest a computer learning model that accurately identifies whether every given DaTscan has PD or not while offering a logical justification for the prediction. Visual indicators are created utilising Local Interpretable Model-Agnostic Explainer (LIME) approaches.…”

“…A variety of AI approaches using ML and DL models have been created in the past. In this study, a new framework is employed by combining grey wolf optimization (GWO) with four deep learning models known as VGG16[ 11 ], DenseNet [ 12 ], InceptionV3[ 13 ], DenseNet-LSTM [ 14 ] and a hybrid model VGG16 + InceptionV3.…”

Parkinson’s Disease Detection by Processing Different ANN Architecture Using Vocal Dataset

“…For the PD diagnosis, machine learning models have been applied to a collection of data sensory system, including handwritten diagrams (Drot, 2014) (Moshkova et al, n.d.) (Basnin & B, 2021), movement (Wahid et al, n.d.), neuroimaging (Wahid et al, n.d.) (Prashanth et al, 2014) (Quan et al, 2019) (Thakur et al, 2022), and voice (Wibawa et al, 2016)(T. J. (Pramanik & Sarker, 2021) (Ozcift, 2012) (Karapinar Senturk, 2020).…”