Kidney stone detection via axial CT imaging: A dataset for AI and deep learning applications

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-03-05 DOI:10.1016/j.dib.2025.111446

Peshraw Ahmed Abdalla , Muhammad Y. Shakor , Aso Khaleel Ameen , Bander Sidiq Mahmood , Nawzad Rasul Hama

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引用次数: 0

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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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.