Visual question answering model for fruit tree disease decision-making based on multimodal deep learning

Article Computer Science, Artificial Intelligence

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

Shui-Hua Wang et al.

Summary: The study aims to develop an AI tool for diagnosing COVID-19 on chest CT images, and the proposed FGCNet model outperforms 15 state-of-the-art methods, showing its effectiveness in assisting radiologists in detecting COVID-19 rapidly.

INFORMATION FUSION (2021)

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Article Computer Science, Artificial Intelligence

MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray

Yu-Dong Zhang et al.

Summary: The study shows that fusing chest CT with X-ray can improve AI diagnosis performance, the proposed MIDCAN method outperforms 8 state-of-the-art approaches, and using multiple modalities of data can achieve better results.

PATTERN RECOGNITION LETTERS (2021)

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Article Computer Science, Artificial Intelligence

Robust hybrid deep learning models for Alzheimer's progression detection

Tamer Abuhmed et al.

Summary: This study introduces two novel deep learning architectures for AD progression detection, one is an interpretable multitask regression model and the other is a hybrid model, both of which demonstrate effectiveness and practicality on ADNI data.

KNOWLEDGE-BASED SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

Evidential fully convolutional network for semantic segmentation

Zheng Tong et al.

Summary: The proposed hybrid architecture combines a fully convolutional network with a Dempster-Shafer layer for image semantic segmentation. Through an end-to-end learning strategy and experiments on three databases, the combination improves the accuracy and calibration of semantic segmentation by assigning confusing pixels to multi-class sets.

APPLIED INTELLIGENCE (2021)

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Article Plant Sciences

Citrus Huanglongbing Detection Based on Multi-Modal Feature Fusion Learning

Dongzi Yang et al.

Summary: A multi-modal feature fusion network combining RGB images and hyperspectral data was proposed in this study to classify citrus leaves with a high accuracy of 97.89%. The concept of multi-source information fusion provides a better way to detect citrus HLB and citrus deficiency.

FRONTIERS IN PLANT SCIENCE (2021)

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Article Geriatrics & Gerontology

ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

Shui-Hua Wang et al.

Summary: The study introduces a novel method for identifying Alzheimer's disease, ADVIAN, utilizing VGG-Inspired network structure and attention mechanisms, combined with 18-way data augmentation, showing superior performance in experiments.

FRONTIERS IN AGING NEUROSCIENCE (2021)

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Article Agriculture, Multidisciplinary