ORCID
https://orcid.org/0009-0005-8413-4149
MSU Affiliations
James Bagley College of Engineering; Department of Electrical and Computer Engineering
Item Type
Research Data
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 5,468 unique 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 in Dhaka, Bangladesh.
This dataset was collected systematically and organized uniquely. Every folder and subfolder has numbered, serially ordered images, making it the first dataset of its kind in terms of structure and usability. This ensures reproducibility and reliability for researchers worldwide.
In total, the dataset is organized into six distinct formats/folders, described below. Two radiologists examined the patients.
Radiologist 1:
- organ_classification_1: Contains 2,775 images of the 10 organs listed above. Designed for organ classification tasks.
- anomaly_detection_1: Contains two sub-folders: normal (2,012 images) and abnormal (796 images). Designed for binary anomaly detection tasks. Images are reorganized from the master folder above.
- organ_classification+anomaly_detection: Contains 2 sub-folders. One represents the normal organs (1,946 images) and one represents abnormal cases (974 images), including a newly added ascites folder. Ascites is a condition where the abdominal cavity becomes overly filled with fluid, and it is classified as a distinct abnormal class. This hybrid dataset is an experimental extension combining both tasks. While curated carefully, users are advised to double-check labels for their specific tasks. Note: this is the master folder for Radiologist 1; all other Radiologist 1 folders are reorganizations of these same images.
Radiologist 2:
- anomaly_detection_2: Contains two sub-folders: normal (656 images) and abnormal (269 images). Total: 925 unique images. This batch was collected independently 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 1,334 images. All unique images not reused from any other folder.
- Patient_Wise: Contains images from 170 patients organized as individual patient folders, each with a split_images subfolder, a total of 2,326 images. Patient-level diagnosis metadata is provided in an accompanying Excel file (.xlsx) and a supplementary text file (Update_Version_01_USG.txt). Of the 170 patients, 150 patients’ images are drawn from Radiologist 2’s existing collection; 20 patients contribute 289 new unique images not present in any other folder. This folder supports patient-level analysis and Multiple Instance Learning (MIL) research.
Unique Image Count Summary:
- Radiologist 1 (master folder): 2,920 images.
- Radiologist 2 (organ_classification_2 + anomaly_detection_2): 2,259 images
- Patient_Wise (20 new patients only): 289 images
- Total unique images: 5,468
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 her father, Dr. Md. Enayet Karim, for his logistical 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.
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. 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.
Publication Date
Fall 11-3-2025
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Karim, Sifat Zina, "Abdominal Ultrasound Image Dataset for Organ Classification and Disease Detection" (2025). Research Data. 5.
https://scholarsjunction.msstate.edu/research-data/5
