Abdominal Ultrasound Image Dataset for Organ Classification and Disease Detection

@article{,
title= {Abdominal Ultrasound Image Dataset for Organ Classification and Disease Detection},
keywords= {abdominal ultrasound, ultrasound images, medical imaging, organ classification, anomaly detection, disease detection, deep learning dataset, medical AI, computer vision, abdominal organs},
author= {},
abstract= {This is a dataset of Ultrasound (US) images of abdominal organs. US imaging is widely accessible and a very common diagnostic tool, as it is non-invasive and does not involve radiation risk. This dataset was curated solely for research in deep learning, with potential applications in supervised, semi-supervised, and unsupervised learning to support disease detection in resource-constrained settings.

The dataset comprises 5,005 images of different abdominal organs, namely: Abdominal Aorta (0), Gallbladder (1), Hepatic Vein (2), Kidneys (3), Liver (4), Ovaries (5), Pancreas (6), Portal Vein (7), Spleen (8), and the Urinary System (9), which includes the Urinary Bladder, Prostate, and Uterus. Images were collected from 563 patients at MH Samorita Medical College and Hospital and in Dhaka, Bangladesh.

The author tried her best to curate this dataset systematically and organize uniquely. Every folder and subfolder has correctly numbered, serially ordered images, making it the first dataset one of its kind in terms of structure and usability. This ensures reproducibility and reliability for researchers worldwide

In total, the dataset is organized into five distinct formats/folders, described below. Two radiologists were examining the patients.

## Radiologist one:

- organ_classification_1: Contains 2,784 images of the 10 organs listed above. Designed for classification tasks.
- anomaly_detection_1: Contains two sub-folders: normal (2,014 images) and abnormal (799 images). Designed for anomaly detection tasks.
- organ_classification+anomaly_detection: Contains 2 sub-folders. One represents the normal organs (1,948 images) and one represents abnormal cases (981 images) including a newly added ascites folder. Ascites is a condition where the abdominal cavity becomes overly filled with fluid, and it was separated as a distinct abnormal class. This hybrid dataset (organ_classification+anomaly_detection) is an experimental extension combining both tasks. While curated carefully, users are advised to double-check labels for their specific tasks.

## Radiologist two:

- anomaly_detection_2: Contains two sub-folders: normal (656 images) and abnormal (269 images). This batch was collected last and was used for semi-supervised anomaly detection tasks.
- organ_classification_2: Contains 10 sub-folders representing the 10 different organs, with a total of 1,293 images.
- Patient_Wise: This folder contains 170 patient images, their diagnosis as metadata in an xlsx file and a text file, Update Version 01_USG.txt. 

## Acknowledgements:

This dataset was developed as part of the author's research under the supervision of Dr. John E. Ball, Mississippi State University. The author is grateful for his guidance, encouragement, and support throughout the course of this work.

The author would like to thank logistical support of her father, Dr. Md. Enayet Karim for his support in coordinating with MH Samorita Medical College and Hospital to obtain the ultrasound images and metadata for this dataset.

The author gratefully acknowledges the contributions of the radiologists, sonographers, and staff at MH Samorita Medical College and Hospital, for their assistance in conducting the ultrasound examinations and providing access to the imaging data. Their efforts in patient care and technical support were invaluable in making this dataset possible.

Ethics and Data Access Permissions: This dataset was collected with formal approval from the Institutional Ethical Review Board (IERB) of MH Samorita Medical College and Hospital, Dhaka, Bangladesh. The ethical clearance was obtained before data collection and written institutional permission was granted before sharing. All images were anonymized and de-identified before inclusion in this dataset, ensuring patient privacy and compliance with ethical research standards.

## License:

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Users are required to cite this dataset when using it in publications, research, or derivative works.

This dataset is openly available for research and academic purposes, supporting reproducibility and transparency in medical AI research.

https://orcid.org/0009-0005-8413-4149

},
terms= {},
license= {https://creativecommons.org/licenses/by/4.0/},
superseded= {},
url= {https://scholarsjunction.msstate.edu/research-data/5/}
}