Top Healthcare Datasets For Machine Learning Is Here That You Should Know

Approximately 90% of all healthcare datasets input data is image data.

It opens up a slew of possibilities for computer vision algorithms to be trained to increase diagnosis accuracy, improve care delivery, or automate medical records administration. Medical data is frequently fragmented, jumbled, and difficult to obtain. Finding appropriate datasets might take hours.


List of Top Healthcare Datasets For Machine Learning


Scientific research and general healthcare Datasets


NLM’s MedPix (


Over 59,000 indexed and curated photos from over 12,000 patients are available for free in our online Medical Image Database.


The Cancer Imaging Archive (TCIA) (


TCIA is a service that de-identifies and makes available to the public a large collection of cancer-related medical healthcare datasets.


Patients’ imaging is classified into “collections” based on a common condition (e.g., lung cancer), image modality or kind (e.g., MRI, CT, digital histopathology, etc.), or research emphasis.


TCIA’s principal file format for radiological imaging is DICOM.


Re3data (


Re3data is a global register of research data repositories that includes repositories from a variety of academic fields. It began in 2012, with funding from the German Research Foundation (DFG) in healthcare datasets.


Over 2000 research themes are represented in Re3Data, which is divided into numerous main groups.


Covid-19 Based Healthcare Datasets


V7 COVID-19 X-Ray dataset (


This dataset contains 6500 pixel-level polygonal lung segmentation from AP/PA chest X-rays. There are 517 COVID-19 instances among them. This dataset contains 6500 pixel-level polygonal lung segmentation from AP/PA chest X-rays. There are 517 COVID-19 instances among them.


Each picture carries a kind of pneumonia (viral, bacterial, fungal, healthy/none) is indicated by a tag.


COVID-19 image dataset (


It’s a COVID-19 healthcare datasets with 137 cleaned pictures and 317 total images of Viral Pneumonia and

Normal Chest X-Rays organized into test and train directories.


COVID-19 CT Scan (


It’s a tiny dataset made up of 20 CT images and expert segmentations of COVID-19 patients.


CT Healthcare Datasets


CT Medical Images (


This dataset contains a tiny part of the cancer imaging archive’s pictures.


The center slice of all CT scans is tagged with age, modality, and contrast. There are 475 series from 69 distinct patients as a result of this.


Deep Lesion (


It is one of the most comprehensive picture collections currently accessible. It includes CT scans obtained from the National Institutes of Health to improve the accuracy of lesion recording and diagnosis. Over 32,000 lesions from over 4000 different patients are included in Deep Lesion.


Public Lung Database (


The present healthcare datasets only has a few annotated CT imaging scans that demonstrate many of the fundamental challenges with quantifying big lung lesions.


All of the photos may be downloaded for free.


VIA Group Public Databases (


It includes two public image files that include DICOM-formatted lung CT scans as well as radiologists’ reporting of anomalies.


MRI Healthcare Datasets




The Offer Access Series of Imaging Studies (OASIS) aims to open up brain MRI healthcare datasets to researchers.


It gives access to a library of neuroimaging and processed imaging data for use in neuroimaging, clinical, and cognitive research on normal aging and cognitive decline over a broad demographic, cognitive, and genetic range.


OASIS-1, OASIS-2, and OASIS-3 are the three datasets currently in the database.


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MRNet: Knee MRI’s (


A total of 1,370 knee MRI tests were done at Stanford University Medical Center for the MRNet dataset.


There are 1,104 aberrant examinations in the healthcare datasets, with 319 ACL injuries and 508 meniscal tears. Manual extraction of labels from clinical records was used to get all of the labels.


IVDM3Seg (


It comprises 24 3D multi-modality MRI data sets of at least 7 lower spine IVDs, gathered from 12 patients in two rounds of a study exploring the effect of extended on the lumbar intervertebral discs, bed rest (spaceflight simulation).


There are 96 high-resolution 3D MRI volume data in total. A binary mask is given for each IVD as a reference manual segmentation. The Neuroimaging Informatics Technology Initiative (NIFTI) file format is used to record all pictures (four volumes per patient) and binary masks (one binary volume per patient).




100,000 Chest X-Rays from the National Institutes of Health (


This collection comprises approximately 112,000 X-ray scans of the chest from over 30,000 different people.


ChestX-Det-Dataset (

Chest X-Ray dataset with instance-level annotations includes 3,578 pictures with instance-level annotations of 13 disease/abnormality categorieshealthcare datasets.


Atelectasis, Calcification, Cardiomegaly, Consolidation, Diffuse Nodule, Effusion, Emphysema, Fibrosis, Fracture, Mass, Nodule, Pleural Thickening, and Pneumothorax are among the thirteen types.


CheXpert (


CheXpert is a dataset made up of 224,316 chest radiographs taken from 65,240 individuals at Stanford University Medical Center between October 2002 and July 2017.


It also contains radiological reports.


SCR database: Chest Radiograph Segmentation (


This database contained digitized chest X-ray pictures with lung field, heart, and clavicle segmentations. All chest radiographs were obtained from the JSRT healthcare datasets, which contains 247 PA chest radiographs from 13 Japanese institutions and one from the United States.


There are 154 photos with precisely one pulmonary lung nodule apiece, whereas the remaining 93 photographs have none.


MURA: MSK Xrays (


MURA is a musculoskeletal radiograph collection of 40,561 multi-view radiographic images in total. It contains 14,863 studies from 12,173 individuals. The elbow, finger, forearm, hand, humerus, shoulder, and wrist are the seven standard upper extremities radiography study types.




The STARE (Structured Analysis of the Retina) dataset is a vascular segmentation healthcare datasets for the retina. In 1975, Michael Goldbaum, M.D., of the University of California, San Diego, designed and launched the STARE Project, which was supported by the US National Institutes of Health.


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