Convolutional Neural Network–Vision Transformer Architecture with Gated Control Mechanism and Multi-Scale Fusion for Enhanced Pulmonary Disease Classification

Through extensive experiments and ablation studies, we demonstrated the effectiveness of our approach in improving the medical image classification performance, while achieving good calibration results. This hybrid approach offers a promising framework for reliable and accurate disease diagnosis in medical imaging.

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Publication Details

Journal
Diagnostics
Published
2024-12-12
DOI
https://doi.org/10.3390/diagnostics14242790
Citations
16
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
6.47

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article

Convolutional Neural Network–Vision Transformer Architecture with Gated Control Mechanism and Multi-Scale Fusion for Enhanced Pulmonary Disease Classification

Xiaopeng Yang, Okpala Chibuike
16 citations
Diagnostics
COVID-19 diagnosis using AI
6.47
article

Convolutional Neural Network–Vision Transformer Architecture with Gated Control Mechanism and Multi-Scale Fusion for Enhanced Pulmonary Disease Classification

Xiaopeng Yang, Okpala Chibuike
article en
16 citations

Abstract

Through extensive experiments and ablation studies, we demonstrated the effectiveness of our approach in improving the medical image classification performance, while achieving good calibration results. This hybrid approach offers a promising framework for reliable and accurate disease diagnosis in medical imaging.

DiagnosticsVol. 14(24)
Handong Global University (KR)
National Research Foundation of Korea
Openalex Percentile: Top 3%
COVID-19 diagnosis using AI
6.47
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