Abstract: Provided is a method for training a neural network to detect features in a retinal image. The method may include the steps of: combining and randomizing feature images into a Training data set; combining and randomizing the feature images into a testing dataset; training a plurality of neural networks having different architectures using a subset of the training dataset while testing on a subset of the testing dataset; identifying the best neural network based on each of the plurality of neural networks performance on the testing data set; inputting images to the best neural network and identifying a limited number of false positives and false negative and adding the false positives and false negatives to the training dataset and testing dataset; and repeating the foregoing steps until an objective performance threshold is reached.

Abstract: Disclosed is a system for qualifying medical images submitted by user for diagnostic analysis comprising: an image input module, configured to receive one or more image input by a user; an image protocol conformation module, configured to receive the one or more images from the image input module, and further configured to analyze each of the one or more images for conformity with a predefined protocol and wherein images that do not conform to the predefined protocol are flagged as non-conforming images; an image output module, configured to identify to the user each of the one or more images flagged as non-conforming and prompting the user to resubmit a new image for each of the non-conforming images; and an image resubmission module, configured to receive the user resubmitted image and provide the resubmitted image to the image protocol conformation module.

Abstract: Provide are systems methods and devices for diagnosing disease in medical images.