Journal: Data in Brief

Article Title: A benchmark dataset of Primitive Indian Paddy Panicle Images and identification via deep residual transfer learning

doi: 10.1016/j.dib.2026.112774

Figure Lengend Snippet: Illustration of 22 varieties of panicle images of primitive paddy.

Article Snippet: Mendeley Data Primitive Indian Paddy Panicle Images (Original data) .

Techniques:

Journal: Data in Brief

Article Title: Dry Fish Image dataset: Data-driven analysis and deep learning-based classification

doi: 10.1016/j.dib.2026.112683

Figure Lengend Snippet: Class distribution of the Dry Fish Dataset.

Article Snippet: Mendeley Data Dry Fish Image Dataset (Original data) .

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Journal: Data in Brief

Article Title: An open-access fire detection image dataset for research in computer vision and safety monitoring

doi: 10.1016/j.dib.2026.112801

Figure Lengend Snippet: The four categories of image samples included in the Fire Recognition Image Dataset . Top row: (a) Fire – images showing active flames; (b) Smoke – images containing visible smoke plumes. Bottom row: (c) Safe Fire – non-hazardous scenes such as candles or stoves; (d) Artificial Fire – simulated or decorative fire displays.

Article Snippet: Mendeley Data Fire Recognition Image Dataset (Original data)

Techniques:

Journal: Data in Brief

Article Title: An open-access fire detection image dataset for research in computer vision and safety monitoring

doi: 10.1016/j.dib.2026.112801

Figure Lengend Snippet: Workflow illustrating the step-by-step process for generating the Fire Recognition Image Dataset, from video selection to dataset organization and augmentation.

Article Snippet: Mendeley Data Fire Recognition Image Dataset (Original data)

Techniques: Selection

Journal: Data in Brief

Article Title: AsianVehicle: An image dataset of traditional Asian vehicles

doi: 10.1016/j.dib.2026.112709

Figure Lengend Snippet: Sample images of AsianVehicle each class.

Article Snippet: Mendeley Data AsianVehicle: Image Dataset of Traditional Asian Vehicles for Computer Vision (Original data) .

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Journal: Scientific Reports

Article Title: Federated learning with swarm intelligence for efficient and secure medical image analysis

doi: 10.1038/s41598-026-50882-8

Figure Lengend Snippet: Cumulative \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document} over communication rounds for the COVID-19 dataset with target \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon = 1.9$$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta = 10^{-5}$$\end{document} . The RDP accountant tracks multiple \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} orders and yields the final \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document} at round 75.

Article Snippet: This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms.

Techniques:

Journal: Scientific Reports

Article Title: Federated learning with swarm intelligence for efficient and secure medical image analysis

doi: 10.1038/s41598-026-50882-8

Figure Lengend Snippet: Performance analysis for Hospital Model A (Client A) across COVID-19, Monkeypox, and Breast Cancer datasets. The radar charts display accuracy, precision, recall, specificity, and F1-score metrics for different CNN-optimization combinations.

Article Snippet: This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms.

Techniques:

Journal: Scientific Reports

Article Title: Federated learning with swarm intelligence for efficient and secure medical image analysis

doi: 10.1038/s41598-026-50882-8

Figure Lengend Snippet: Performance evaluation for Hospital Model B (Client B) showing superior COVID-19 detection capabilities with VGG16 + PSO and exceptional Monkeypox classification using Inception + PSO across all evaluation metrics.

Article Snippet: This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms.

Techniques:

Journal: Scientific Reports

Article Title: Federated learning with swarm intelligence for efficient and secure medical image analysis

doi: 10.1038/s41598-026-50882-8

Figure Lengend Snippet: Hospital Model C (Client C) performance analysis demonstrating exceptional consistency across all medical datasets, with VGG16 + PSO excelling in COVID-19 detection and Inception + PSO achieving superior Monkeypox classification accuracy.

Article Snippet: This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms.

Techniques:

Journal: Scientific Reports

Article Title: Federated learning with swarm intelligence for efficient and secure medical image analysis

doi: 10.1038/s41598-026-50882-8

Figure Lengend Snippet: Hospital Model D (Client D) performance evaluation showing exceptional COVID-19 detection with VGG16 + PSO achieving 96.71% accuracy and superior Monkeypox classification using Inception + PSO with balanced precision and recall metrics.

Article Snippet: This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms.

Techniques:

Journal: European Radiology Experimental

Article Title: Feasibility of bone-like MRI for acetabular morphology assessment in developmental dysplasia of the hip

doi: 10.1186/s41747-026-00725-y

Figure Lengend Snippet: Results of JSW measurements. In the 32 hips analyzed in this study, the mean JSW measured on MRI was 4.2 ± 1.4 mm on T1-weighted images (T1WI), 3.0 ± 1.3 mm on STIR, and 3.3 ± 0.9 mm on MERGE images. On CT images, the mean JSW was 3.4 ± 1.1 mm. CT, Computed tomography; MERGE, Multi-echo recombined gradient echo; STIR, Short tau inversion-recovery; T1-WI, T1-weighted imaging

Article Snippet: Similar sequences from other manufacturers include “multi-echo data image combination”‒MEDIC from Siemens Healthineers and “merged fast field echo”‒M-FFE from Philips Healthcare.

Techniques: Computed Tomography, Imaging

Journal: European Radiology Experimental

Article Title: Feasibility of bone-like MRI for acetabular morphology assessment in developmental dysplasia of the hip

doi: 10.1186/s41747-026-00725-y

Figure Lengend Snippet: Bland–Altman analysis comparing MERGE MRI and CT measurements. a Bland–Altman plot showing agreement between MERGE MRI and CT measurements for JSW. The solid horizontal line represents the mean difference (-0.25 mm), and the dashed lines indicate the 95% limits of agreement (-2.04 to 1.54 mm). No proportional bias was observed across the measurement range. b Bland–Altman plot showing agreement between MERGE MRI and CT measurements for the CE angle. The solid horizontal line represents the mean difference (-0.09°), and the dashed lines indicate the 95% limits of agreement (-9.85° to 9.66°). No systematic or proportional bias was observed. CE, Center-edge; CT, computed tomography; MERGE, Multi-echo recombined gradient echo

Article Snippet: Similar sequences from other manufacturers include “multi-echo data image combination”‒MEDIC from Siemens Healthineers and “merged fast field echo”‒M-FFE from Philips Healthcare.

Techniques: Computed Tomography

Journal: European Radiology Experimental

Article Title: Feasibility of bone-like MRI for acetabular morphology assessment in developmental dysplasia of the hip

doi: 10.1186/s41747-026-00725-y

Figure Lengend Snippet: Results of CE angle measurements. In the 32 hips analyzed, the mean CE angle measured on MRI was 29.7 ± 9.5° on T1WI, 30.9 ± 9.9° on STIR, and 31.4 ± 10.1° on MERGE images. On CT, the mean CE angle was 31.4 ± 10.1°. CT, Computed tomography; MERGE, Multi-echo recombined gradient echo; STIR, Short tau inversion-recovery; T1-WI, T1-weighted imaging

Article Snippet: Similar sequences from other manufacturers include “multi-echo data image combination”‒MEDIC from Siemens Healthineers and “merged fast field echo”‒M-FFE from Philips Healthcare.

Techniques: Computed Tomography, Imaging

Journal: European Radiology Experimental

Article Title: Feasibility of bone-like MRI for acetabular morphology assessment in developmental dysplasia of the hip

doi: 10.1186/s41747-026-00725-y

Figure Lengend Snippet: Representative clinical case. A 23-year-old female patient underwent MRI and CT examinations several months after right hip PAO. On coronal images: ( a ) T1WI shows a JSW of 4.9 mm; ( b ) STIR shows 3.6 mm; ( c ) MERGE shows 4.0 mm; and ( d ) CT shows 4.2 mm. The CE angles were: 36° on T1WI, 33° on STIR, 30° on MERGE, and 30° on CT. CT, Computed tomography; MERGE, Multi-echo recombined gradient echo; STIR, Short tau inversion-recovery; T1-WI, T1-weighted imaging

Article Snippet: Similar sequences from other manufacturers include “multi-echo data image combination”‒MEDIC from Siemens Healthineers and “merged fast field echo”‒M-FFE from Philips Healthcare.

Techniques: Computed Tomography, Imaging