Automated Parkinson's Disease Detection: A Review of Techniques, Datasets, Modalities, and Open Challenges DOI Open Access
Sheerin Zadoo, Yashwant Singh, Pradeep Kumar Singh

et al.

International Journal on Smart Sensing and Intelligent Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: Jan. 1, 2024

Abstract Parkinson's disease (PsD) is a prevalent neurodegenerative malady, which keeps intensifying with age. It acquired by the progressive demise of dopaminergic neurons existing in substantia nigra pars compacta region human brain. In absence single accurate test, and due to dependency on doctors, intensive research being carried out automate early detection predict severity also. this study, detailed review various artificial intelligence (AI) models applied different datasets across modalities has been presented. The emotional (EI) modality, can be used for help maintaining comfortable lifestyle, identified. EI predominant, emerging technology that detect PsD at initial stages enhance socialization patients their attendants. Challenges possibilities assist bridging differences between fast-growing technologies meant actual implementation automated model are presented research. This highlights prominence using support vector machine (SVM) classifier achieving an accuracy about 99% many such as magnetic resonance imaging (MRI), speech, electroencephalogram (EEG). A 100% achieved EEG handwriting modality convolutional neural network (CNN) optimized crow search algorithm (OCSA), respectively. Also, 95% progression Bagged Tree, (ANN), SVM. maximum attained K-nearest Neighbors (KNN) Naïve Bayes classifiers signals EI. most widely dataset identified Progression Markers Initiative (PPMI) database.

Language: Английский

Deep Learning in Medical Imaging for Early Disease Detection DOI
Saadaldeen Rashid Ahmed, Talib A. Al-Sharify, Taif S. Hasan

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 369 - 381

Published: Jan. 1, 2025

Language: Английский

Citations

0
Automated Detection of Canine Babesia Parasite in Blood Smear Images Using Deep Learning and Contrastive Learning Techniques DOI Creative Commons

Dilip Kumar Baruah,

Kuntala Boruah,

Nagendra Nath Barman

et al.

Parasitologia, Journal Year: 2025, Volume and Issue: 5(2), P. 23 - 23

Published: May 14, 2025

This research introduces a novel method that integrates both unsupervised and supervised learning, leveraging SimCLR (Simple Framework for Contrastive Learning of Visual Representations) self-supervised learning along with different pre-trained models to improve microscopic image classification Babesia parasite in canines. We focused on three popular CNN architectures, namely ResNet, EfficientNet, DenseNet, evaluated the impact pre-training their performance. A detailed comparison variants Densenet terms accuracy training efficiency is presented. Base such as DenseNet were utilized within framework. Firstly, unlabeled images, followed by classifiers labeled datasets. approach significantly improved robustness accuracy, demonstrating potential benefits combining contrastive conventional techniques. The highest 97.07% was achieved Efficientnet_b2. Thus, detection or other hemoparasites blood smear images could be automated high without using labelled dataset.

Language: Английский

Citations

0
TPat: Transition pattern feature extraction based Parkinson’s disorder detection using FNIRS signals DOI
Türker Tuncer, İrem Taşçı, Burak Taşçı

et al.

Applied Acoustics, Journal Year: 2024, Volume and Issue: 228, P. 110307 - 110307

Published: Sept. 27, 2024

Language: Английский

Citations

3
HBNet: an integrated approach for resolving class imbalance and global local feature fusion for accurate breast cancer classification DOI
Barsha Abhisheka, Saroj Kr. Biswas, Biswajit Purkayastha

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(15), P. 8455 - 8472

Published: Feb. 22, 2024

Language: Английский

Citations

2
Automated Parkinson's Disease Detection: A Review of Techniques, Datasets, Modalities, and Open Challenges DOI Open Access
Sheerin Zadoo, Yashwant Singh, Pradeep Kumar Singh

et al.

International Journal on Smart Sensing and Intelligent Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: Jan. 1, 2024

Abstract Parkinson's disease (PsD) is a prevalent neurodegenerative malady, which keeps intensifying with age. It acquired by the progressive demise of dopaminergic neurons existing in substantia nigra pars compacta region human brain. In absence single accurate test, and due to dependency on doctors, intensive research being carried out automate early detection predict severity also. this study, detailed review various artificial intelligence (AI) models applied different datasets across modalities has been presented. The emotional (EI) modality, can be used for help maintaining comfortable lifestyle, identified. EI predominant, emerging technology that detect PsD at initial stages enhance socialization patients their attendants. Challenges possibilities assist bridging differences between fast-growing technologies meant actual implementation automated model are presented research. This highlights prominence using support vector machine (SVM) classifier achieving an accuracy about 99% many such as magnetic resonance imaging (MRI), speech, electroencephalogram (EEG). A 100% achieved EEG handwriting modality convolutional neural network (CNN) optimized crow search algorithm (OCSA), respectively. Also, 95% progression Bagged Tree, (ANN), SVM. maximum attained K-nearest Neighbors (KNN) Naïve Bayes classifiers signals EI. most widely dataset identified Progression Markers Initiative (PPMI) database.

Language: Английский

Citations

2