Acne Grading API

[General Instructions] | [Demo]

Acne is the commonest ‘disease’, but severe forms are associated with considerable morbidity. Dermatologists grade acne according to severity to decide when to intervene and treat it. As acne is extremely common, computer-assisted acne grading is a popular machine-learning task.

So far the popular approach has been to create CNN-based classification models. It is difficult to train and deploy these models and the clinical utility is limited. There have also been regression-based approaches as the grades are ‘ranks’ than ‘classes’. We adopt an object detection-based approach that categorizes acne lesions into four basic types: comedones, papulopustular, nodulocystic and scars.

After characterizing and counting these subtypes, we use a weighted counting to reduce it to a single numeric grade between 0-8. The API returns the submitted image, along with the lesion counts and this single numeric grade.

This API is suitable for cosmetic dermatology service providers to document progress and to screen clients to identify severe cases. The detection sensitivity can be adjusted according to the needs.

Connect on RapidAPI


The scar detection is suboptimal. We will try to improve this in future versions. The backend improvements may not require any changes in the frontend.

Image requirements:

Submit a close-up image of the side of the face with neutral lighting. Try it out few times to find an appropriate sensitivity for your use case and image acquisition system.


Output includes the image with the detected lesions boxed. Additionally, the counts of the four lesion subclasses and an overall numeric grade are returned.

Sample output

{"function": "AcneGrading", "image": "data:image/jpeg;base64, /9j/4AAQSkZJRgABAQEAZABkAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQoKCgoKBggLDAsKDAkKCgr/2wBDAQICAgICAgUD9k=", "comedone": 17, "papulopustular": 9, "nodulocystic": 0, "scar": 0, "acne_grade": 0}
API Demo
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