ArticleData in brief2025
Kidney stone detection via axial CT imaging: A dataset for AI and deep learning applications.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A fully optimized deep learning framework for explainable and efficient kidney stone detection in computed tomography imaging.Abdominal radiology (New York) · 2026Article
- Artificial Intelligence in Renal Imaging: A Multi-Dataset Study for Kidney Disease Classification.Biomedicines · 2026Article
- NephroNet: a calibration-aware, patient-disjoint benchmark for multiclass kidney CT classification with a compact depthwise-separable CNN.Frontiers in medicine · 2026Article
- Pomegranate disease detection and classification dataset for deep learning applications: A case study from Halabja city.Data in brief · 2025Article
- Kidney stone detection via axial CT imaging: A dataset for AI and deep learning applications.Data in brief · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article introduces a comprehensive CT scan image dataset focused on kidney stone detection, consisting of two groups: one drawn from patients with kidney stones and the other from patients without kidney stones. This dataset has been cleaned, cross-checked, and checked adequately before labeling in coordination with the medical experts from the medical field. Samples in the dataset were derived from different health facilities in Sulaimani and Rania, Iraq, which supplied crucial information about the demographics and patterns of kidney stones in the area. It holds 3364 original CT images and 35,457 augmented CT images, which can be used to create deep-learning models for kidney stone diagnosis. The enhanced images also make it possible to use them in training or developing medical practice and educational algorithms. This dataset can be an asset in developing new diagnostic tools, supporting medical research, and being used as learning material for students studying in the medical field.
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