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Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network.
Comput Biol Med
January 2025
University of Rwanda, Rwanda. Electronic address:
Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational resources, and the need for extensive datasets. To tackle these issues, we introduced two novel algorithms: the Dynamic Co-Occurrence Grey Level Matrix (DC-GLM) and the Contextual Adaptation Multiscale Gabor Network (CAMSGNeT).
View Article and Find Full Text PDFFeature enhanced cascading attention network for lightweight image super-resolution.
Sci Rep
January 2025
Zhongyu (Fujian) Digital Technology Co., Ltd, Fuzhou, 350108, China.
Attention mechanisms have been introduced to exploit deep-level information for image restoration by capturing feature dependencies. However, existing attention mechanisms often have limited perceptual capabilities and are incompatible with low-power devices due to computational resource constraints. Therefore, we propose a feature enhanced cascading attention network (FECAN) that introduces a novel feature enhanced cascading attention (FECA) mechanism, consisting of enhanced shuffle attention (ESA) and multi-scale large separable kernel attention (MLSKA).
View Article and Find Full Text PDFFrom tiny to immense: Geological spotlight on the Alexander Mosaic (National Archaeological Museum of Naples, Italy) using non-invasive in situ analyses.
PLoS One
January 2025
Dipartimento di Architettura, University of Naples Federico II, Naples, Italy.
A key challenge in the art and archaeological field is the instrumental analysis of objects and materials while preserving their integrity. In this study, the world-renowned artwork Alexander Mosaic (The Issus Battle, collection of the National Archaeological Museum of Naples, IT), the most iconic representation of the face of the Macedonian king Alexander the Great coming from a Pompeii domus, was thoroughly analyzed with mobile and non-invasive methods, within a great project of restoration started in 2020. Representative areas of the Mosaic, overall consisting of ca.
View Article and Find Full Text PDFSparse keypoint segmentation of lung fissures: efficient geometric deep learning for abstracting volumetric images.
Int J Comput Assist Radiol Surg
January 2025
Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
Purpose: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We propose to make lung fissure segmentation more efficient by using geometric deep learning (GDL) on sparse point clouds.
View Article and Find Full Text PDFA Scene Knowledge Integrating Network for Transmission Line Multi-Fitting Detection.
Sensors (Basel)
December 2024
Automation Department, North China Electric Power University, Baoding 071003, China.
Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings.
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