CNN-Based Feature-Level Fusion of Very High Resolution Aerial Imagery and Lidar Data

Daneshtalab, S.; Rastiveis, H.; Hosseiny, Benyamin

doi:10.5194/isprs-archives-xlii-4-w18-279-2019

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Machine learning-based methods (CNN) of multimodality fusion is an effective medical image analysis method (Mathotaarachchi et al 2017 ; Huang et al 2019 ; Jiang et al 2021 ; Liu et al 2018 ) for multi-class classification (Goenka and Tiwari 2022b , c ). Authors in Daneshtalab et al ( 2019 ) produced an accuracy of 94.2% which is a better performance than that (Qiu et al 2018 ) with an accuracy of 84.0%. Both studies fused information extracted from sMRI and DTI images, but the study with machine learning-based methods (Kang et al 2020 ) performed better.…”

Section: Discussionmentioning

confidence: 81%

“…A preferred abstraction by most of the researchers was feature-level fusion due to its capability of proving more valid results in the case of compatible features (Daneshtalab et al 2019 ; Agarwal and Desai 2021 ). However, the concatenation of compatible features may produce an extremely feature vector that makes the computational load more difficult (Nachappa et al Apr.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Odusami,

Maskeliūnas,

Damaševičius

et al. 2023

Cogn Neurodyn

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In recent years, Alzheimer’s disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87–87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Odusami,

Maskeliūnas,

Damaševičius

et al. 2023

Cogn Neurodyn

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show abstract

“…Recent work has also explored the fusion of airborne Li-DAR with overhead imagery for the task of semantic segmentation in an urban area [2]. Typically these approaches render the LiDAR data as 2D images through digital surface models and use a traditional CNN.…”

Section: Multi-modal Data Fusionmentioning

confidence: 99%

RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery

Hadžić

Blanton

Song

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Roadway free-flow speed captures the typical vehicle speed in low traffic conditions. Modeling free-flow speed is an important problem in transportation engineering with applications to a variety of design, operation, planning, and policy decisions of highway systems. Unfortunately, collecting large-scale historical traffic speed data is expensive and time consuming. Traditional approaches for estimating free-flow speed use geometric properties of the underlying road segment, such as grade, curvature, lane width, lateral clearance and access point density, but for many roads such features are unavailable. We propose a fully automated approach, RasterNet, for estimating free-flow speed without the need for explicit geometric features. RasterNet is a neural network that fuses large-scale overhead imagery and aerial LiDAR point clouds using a geospatially consistent raster structure. To support training and evaluation, we introduce a novel dataset combining free-flow speeds of road segments, overhead imagery, and LiDAR point clouds across the state of Kentucky. Our method achieves state-ofthe-art results on a benchmark dataset.

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Dcrff-Lhrf: an improvised methodology for efficient land-cover classification on eurosat dataset

Bhatt,

Bhatt

2023

Multimed Tools Appl

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CNN-Based Feature-Level Fusion of Very High Resolution Aerial Imagery and Lidar Data

Cited by 5 publications

References 19 publications

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery

Dcrff-Lhrf: an improvised methodology for efficient land-cover classification on eurosat dataset

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