Deep Learning for the Assisted Diagnosis of Movement Disorders, Including Isolated Dystonia

Bashir, Syed Muhammad Arsalan; Wang, Yi

doi:10.3389/fneur.2021.638266

Cited by 9 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Assisted diagnosis using super-resolution has been a recent trend; for instance, researchers used deep learning-based SR methods to assist the diagnosis of movement disorders like isolated dystonia ( Bashir & Wang, 2021a ).…”

Section: Domain-specific Applications Of Super-resolutionmentioning

confidence: 99%

“…Initially, this technique was classified as spatial resolution enhancement ( Tsai & Huang, 1984 ). The applications of super-resolution include computer graphics ( Kim, Lee & Lee, 2016a , b ; Tao et al, 2017 ), medical imaging ( Bates et al, 2007 ; Fernández-Suárez & Ting, 2008 ; Huang et al, 2008 ; Hamaide et al, 2017 ; Jurek et al, 2020 ; Teh et al, 2020 ; Bashir & Wang, 2021a ), security, and surveillance ( Zhang et al, 2010 ; Shamsolmoali et al, 2018 ; Lee, Kim & Heo, 2020 ), which shows the importance of this topic in recent years.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive review of deep learning-based single image super-resolution

Bashir

Wang

Khan

et al. 2021

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

show abstract

Section: Domain-specific Applications Of Super-resolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comprehensive review of deep learning-based single image super-resolution

Bashir

Wang

Khan

et al. 2021

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, deep learning-based methods have been successfully implemented in classification, detection, and recognition tasks (Bao et al, 2020;Bashir & Wang, 2021a;Z. Lin et al, 2019;Wei & Liu, 2021).…”

Section: Universal Object Detectorsmentioning

confidence: 99%

“…Object detection and recognition have been a core problem in computer vision, and it aims to localize the objects within an image. As far as remote sensing is concerned in the past decade, object detection is a crucial task that is closely related to various applications, for instance, geographic mapping of resources, crop harvest analysis, disaster management, traffic planning, and navigation (Bai et al, 2021;Bashir & Wang, 2021b, 2021aKussul et al, 2017;Pi et al, 2020;Wei & Liu, 2021). Due to its comprehensive coverage, remote sensing images have various objects of interest ranging from small to large-sized objects.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Super-resolution Object Detection (RSSOD) Dataset

2021

View full text Add to dashboard Cite

For the past two decades, there have been significant efforts to develop methods for object detection in Remote Sensing (RS) images. In most cases, the datasets for small object detection in remote sensing images are inadequate. Many researchers used scene classification datasets for object detection, which has its limitations; for example, the large-sized objects outnumber the small objects in object categories. Thus, they lack diversity; this further affects the detection performance of small object detectors in RS images. This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images. We also propose a large-scale, publicly available benchmark Remote Sensing Super-resolution Object Detection (RSSOD) dataset. The RSSOD dataset consists of 1,759 hand-annotated images with 22,091 instances of very high resolution (VHR) images with a spatial resolution of ~0.05 m. There are five classes with varying frequencies of labels per class; the images are annotated in YOLO and COCO format. The image patches are extracted from satellite images, including real image distortions such as tangential scale distortion and skew distortion. The proposed RSSOD Preprint submitted to an Elsevier Journal for Review 2 dataset will help researchers benchmark the state-of-the-art object detection methods across various classes, especially for small objects using image super-resolution. We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolutionbased object detection and compare with the existing state-of-the-art methods based on image super-resolution (SR). The proposed MCGR achieved state-of-the-art performance for image SR with an improvement of 1.2dB PSNR compared to the current state-of-the-art NLSN method. MCGR achieved best object detection mAPs of 0.758, 0.881, 0.841, and 0.983, respectively, for five-class, four-class, two-class, and single classes, respectively surpassing the performance of the state-of-the-art object detectors YOLOv5, EfficientDet, Faster RCNN, SSD, and RetinaNet.

show abstract

“…The rapid advancement of science and technology has made remote-sensing image technology indispensable for various applications. Some examples of these applications include monitoring for diseases, transportation planning, environmental monitoring, crop harvest analysis, geological surveys, and identifying objects used in military operations [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review

Wang

Song

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

With the accelerated development of artificial intelligence, remote-sensing image technologies have gained widespread attention in smart cities. In recent years, remote sensing object detection research has focused on detecting and counting small dense objects in large remote sensing scenes. Small object detection, as a branch of object detection, remains a significant challenge in research due to the image resolution, size, number, and orientation of objects, among other factors. This paper examines object detection based on deep learning and its applications for small object detection in remote sensing. This paper aims to provide readers with a thorough comprehension of the research objectives. Specifically, we aggregate the principal datasets and evaluation methods extensively employed in recent remote sensing object detection techniques. We also discuss the irregularity problem of remote sensing image object detection and overview the small object detection methods in remote sensing images. In addition, we select small target detection methods with excellent performance in recent years for experiments and analysis. Finally, the challenges and future work related to small object detection in remote sensing are highlighted.

show abstract

Deep Learning for the Assisted Diagnosis of Movement Disorders, Including Isolated Dystonia

Cited by 9 publications

References 14 publications

A comprehensive review of deep learning-based single image super-resolution

A comprehensive review of deep learning-based single image super-resolution

Remote Sensing Super-resolution Object Detection (RSSOD) Dataset

Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review

Contact Info

Product

Resources

About