CT Imaging Lung Nodule Detection Dataset
#Object detection #computer vision #deep learning #Lung nodule detection #medical image analysis #disease diagnosis assistance
- 500 records
- 1.6G
- DICOM
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-14
AI Analysis & Value Prop
In the healthcare sector, lung nodule detection is a significant and highly challenging task. With the advancement of CT technology, doctors face the pressure of quickly diagnosing and accurately detecting lung nodules for timely disease intervention. However, current manual detection processes are time-consuming and prone to error, while existing algorithms have limited detection rates in low contrast and complex backgrounds. The CT Imaging Lung Nodule Detection Dataset aims to provide high-quality training data to improve the accuracy and efficiency of automated detection systems. The dataset is collected through high-resolution CT scanners in clinical environments, ensuring data authenticity and diversity. To ensure data quality, multiple rounds of annotation and consistency checks are conducted, reviewed by an experienced team of radiologists. The annotation team includes ten senior doctors, each with over ten years of professional experience. Data preprocessing includes noise reduction, normalization, and slicing, orderly stored in DICOM format within the machine learning pipeline.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| nodule_type | string | Describes the type of lung nodule, such as solid nodule, sub-solid nodule, etc. |
| nodule_size | float | The size of the lung nodule, usually measured in millimeters. |
| nodule_location | string | Location of the lung nodule in the image, such as left upper lobe, right lower lobe, etc. |
| nodule_shape | string | The shape characteristics of the lung nodule, such as round, oval, etc. |
| nodule_margin | string | The characteristics of the nodule edge, such as smooth, rough, etc. |
| nodule_density | string | Describes the density of the nodule, such as high density, low density, etc. |
| calcification | boolean | Indicates whether there is calcification in the nodule. |
| vascular_signs | string | Describes the relationship characteristics between the nodule and surrounding vessels. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What is the main purpose of the CT Lung Nodule Detection dataset?
- The dataset is primarily used to improve the detection accuracy of lung nodules in the medical health field, aiding doctors in early discovery and diagnosis of lung diseases.
- What technology is used for the image data in this dataset?
- The image data in the dataset is sourced from CT (Computed Tomography) technology, which is an imaging technique used for diagnosing diseases.
- How does this dataset help improve the accuracy of lung nodule detection?
- By providing a large amount of annotated CT image data, machine learning models can learn and identify the characteristics of lung nodules, thus improving detection accuracy and reliability.
- What preliminary preparations are needed to use the CT Lung Nodule Detection dataset?
- Having a certain level of medical background knowledge and skills in image processing and machine learning is necessary to better understand the data and apply it in algorithm training.
- What are the application scenarios of the CT Lung Nodule Detection dataset in research?
- The dataset can be used for developing and validating automated lung nodule detection systems, improving the speed of doctor diagnosis, and exploring new medical image analysis techniques in academic research.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={CT Imaging Lung Nodule Detection Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/43f1554ccd21c640bf452fac29c5cdaa},
urldate={2026-02-04},
keywords={CT imaging, lung nodule detection, medical AI, object detection, medical image analysis},
version={1.0}
}Using this in research? Please cite us.
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