A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions

Yang, Yuan; Zhang, Lin; Du, Mengnan; Bo, Jingyu; Liu, Haolei; Ren, Lei; Li, Xiaohe; Deen, M. Jamal

doi:10.1016/j.compbiomed.2021.104887

Cited by 51 publications

(19 citation statements)

References 40 publications

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“…Despite this reduction in the number of parameters, the literature has been proofing that MobileNet V2 is equally capable. A comparative analysis on different DL architectures for identifying COVID-19 in patient lung images proves that MobileNet V2 and ResNet are equally capable and VGG has a severe accuracy dropping [33]. This comparative work is relevant to us because of its wide analysis in a difficult task similar to ours, where the network has to identify details to distinguish between the different classes.…”

Section: Data Training and Evaluationmentioning

confidence: 68%

Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

et al. 2023

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Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialised labour is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods such as genetic analysis or ampelometry are time-consuming, expensive and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by non-experts in ampelometry. To this end, Deep Learning (DL) and Machine Learning (ML) approaches have been successfully applied for classification purposes. The present work extends the state-of-the-art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34 and VGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines' varieties through the leaf with a weighted F1 score higher than 92 %.

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Section: Data Training and Evaluationmentioning

confidence: 68%

Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

et al. 2023

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“…The DenseNet model is developed based on the same basic idea as ResNet, but it establishes dense connections between all of the previous and subsequent layers, which is reflected in its name. These features allow DenseNet to achieve better performance than ResNet with fewer parameters and less computational cost [ 52 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms

Oppong

Twum

Hayfron-Acquah

et al. 2022

Computational Intelligence and Neuroscience

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Computer vision is the science that enables computers and machines to see and perceive image content on a semantic level. It combines concepts, techniques, and ideas from various fields such as digital image processing, pattern matching, artificial intelligence, and computer graphics. A computer vision system is designed to model the human visual system on a functional basis as closely as possible. Deep learning and Convolutional Neural Networks (CNNs) in particular which are biologically inspired have significantly contributed to computer vision studies. This research develops a computer vision system that uses CNNs and handcrafted filters from Log-Gabor filters to identify medicinal plants based on their leaf textural features in an ensemble manner. The system was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana (MyDataset) consisting of forty-nine (49) plant species. Using the concept of transfer learning, ten pretrained networks including Alexnet, GoogLeNet, DenseNet201, Inceptionv3, Mobilenetv2, Restnet18, Resnet50, Resnet101, vgg16, and vgg19 were used as feature extractors. The DenseNet201 architecture resulted with the best outcome of 87% accuracy and GoogLeNet with 79% preforming the worse averaged across six supervised learning algorithms. The proposed model (OTAMNet), created by fusing a Log-Gabor layer into the transition layers of the DenseNet201 architecture achieved 98% accuracy when tested on MyDataset. OTAMNet was tested on other benchmark datasets; Flavia, Swedish Leaf, MD2020, and the Folio dataset. The Flavia dataset achieved 99%, Swedish Leaf 100%, MD2020 99%, and the Folio dataset 97%. A false-positive rate of less than 0.1% was achieved in all cases.

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“…Over the past several years, DNNs have proved to be powerful tools for a wide range of modeling and prediction tasks, and have been shown to be effective in the diagnosis and prognostication of a variety of different pathologies. 113,[119][120][121][122] For example, like the explicable model, a DNN would take biophysical and biochemical inputs from the wound and output a prognosis for the wound. A novel application of neural networking to wound healing is processing simple photographs of wounds into a segmentation mask from which the wound area can be extracted, 113 which aids in negating the significant burden of subjective inaccuracy of the human-performed measurements.…”

Section: Machine Learningmentioning

confidence: 99%

“…128 Moreover, there are a variety of commercially available architecture CNNs that have been validated in a variety of clinical settings, ranging from skin wound healing to the detection of lung damage in SARS-CoV2 patients from lung images. 121,129 With the availability of such options, it is necessary to consider the many upshots and pitfalls for each, and develop image analysis pipelines that can reliably and reproducibility used for modeling.…”

Section: Machine Learningmentioning

confidence: 99%

Advances in Non-invasive Biosensing Measures to Monitor Wound Healing Progression

Short¹,

Parikh²,

Colchado³

et al. 2022

Preprint

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Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition include bacterial contamination, tissue perfusion, and mechanical injury and strain. These measures are used by wound-care providers to intervene upon the healing wound to steer healing toward a more functional phenotype with improved structural integrity and healing outcomes and to prevent adverse wound developments.In this review, we discuss bioengineering advances in non-invasive detection of biologic and physiologic factors of the healing wound, visualizing and modeling the ECM, and computational tools to efficiently evaluate the complex data acquired from the wounds to prognosticate healing outcomes and intervene effectively. We focus on bioelectronics and biologic interfaces of the sensors and actuators for real time biosensing and actuation. We also discuss high-resolution, advanced imaging techniques, which go beyond traditional confocal and fluorescence microscopy to visualize microscopic details of the composition of the matrix, linearity of collagen, and live tracking of components within the ECM. Computational modeling of the matrix, including partial differential equation datasets as well as machine learning models that can serve as powerful tools for physicians to guide their decision-making process are discussed.

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A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions

Cited by 51 publications

References 40 publications

Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms

Advances in Non-invasive Biosensing Measures to Monitor Wound Healing Progression

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