Abstract: Disclosed herein are system, method, and computer program product embodiments for distinguishing a disease state from a non-disease state in an image. An embodiment operates by receiving an image of a target area over a network. The embodiment then corrects for background noise in the image by applying a semantic segmentation filter to obtain a segmented image. The sematic segmentation filter may be trained to remove the background noise from the image. The embodiment then determines, using a trained artificial intelligence (AI) model and the segmented image, at least one classification for the target area. The embodiment finally causes the display of the at least one classification and disease information on a user device associated with a user. The trained AI model may be trained using at least augmented images obtained from a set of images to correct for at least an imbalance in the set of images.

Abstract: Disclosed herein are system, method, and computer program product embodiments for distinguishing a disease state from a non-disease state in an image. An embodiment operates by receiving an image of a target area over a network. The embodiment then corrects for background noise in the image by applying a semantic segmentation filter to obtain a segmented image. The sematic segmentation filter may be trained to remove the background noise from the image. The embodiment then determines, using a trained artificial intelligence (AI) model and the segmented image, at least one classification for the target area. The embodiment finally causes the display of the at least one classification and disease information on a user device associated with a user. The trained AI model may be trained using at least augmented images obtained from a set of images to correct for at least an imbalance in the set of images.