Universe

LungAbnormalitiesDetection Computer Vision Model

byKathmandu UniversityTask:
Object Detection
License:CC BY 4.0488 views42 downloads

How to use the LungAbnormalitiesDetection Detection API

Try This Model

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Or try a test image 

Code Snippets

from inference_sdk import InferenceHTTPClient

CLIENT = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key="API_KEY"
)

result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="lungabnormalitiesdetection/1")
Give your agent everything it needs

Or, Use Free Airspace_Opacity, Cavitation and Pleural_Effusion Detection API

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Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "Airspace_Opacity, Cavitation, Pleural_Effusion"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 
See all 946 imagesLungAbnormalitiesDetection LungAbnormalities sample showing Airspace_OpacityLungAbnormalitiesDetection LungAbnormalities sampleLungAbnormalitiesDetection LungAbnormalities sampleLungAbnormalitiesDetection LungAbnormalities sample showing Airspace_OpacityLungAbnormalitiesDetection LungAbnormalities sampleLungAbnormalitiesDetection LungAbnormalities sample showing Airspace_OpacityLungAbnormalitiesDetection LungAbnormalities sampleLungAbnormalitiesDetection LungAbnormalities sampleLungAbnormalitiesDetection LungAbnormalities sampleLungAbnormalitiesDetection LungAbnormalities sample showing Airspace_OpacityLungAbnormalitiesDetection LungAbnormalities sampleLungAbnormalitiesDetection LungAbnormalities sample

About LungAbnormalitiesDetection Model

Here are a few use cases for this project:

  1. Healthcare Diagnostics: The model can be used in hospitals and clinics to assist doctors in diagnosing lung diseases, anomalies, and conditions such as pneumonia or tuberculosis by identifying signs of Airspace_Opacity, Pleural_Effusion, or Cavitation in patients' chest x-rays.

  2. Telemedicine Platforms: The model can be integrated into telemedicine applications to provide remote diagnostics, allowing healthcare professionals to evaluate patients' lung health without the need for physical consultations.

  3. Medical Research: Research institutions could use this model to aid in studying lung diseases and conditions. By analyzing a large dataset of images with the model, researchers can gain insights into different types of lung abnormalities and their prevalence.

  4. Medical Training: This model can be used in educational settings for teaching students about lung abnormalities. Medical schools and training institutions could use the output from the model to show examples of different types of abnormalities in real patient data.

  5. Pharmaceutical Trials: This model could assist in drug testing and development. By identifying and categorizing lung abnormalities in participating patients, pharmaceutical companies can better quantify the effect their drugs have on specific lung conditions.

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ lungabnormalitiesdetection_dataset,
  title = { LungAbnormalitiesDetection Dataset },
  type = { Open Source Dataset },
  author = { Kathmandu University },
  howpublished = { \url{ https://universe.roboflow.com/kathmandu-university-hqa4p/lungabnormalitiesdetection } },
  url = { https://universe.roboflow.com/kathmandu-university-hqa4p/lungabnormalitiesdetection },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2026 },
  month = { jan },
  note = { visited on 2026-07-29 },
}

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