Neural network-based object detection in visual input

Abstract

A device to detect an object in a medical image is described. An image analysis application, executed by the device, receives the medical image as an input. The medical image is next partitioned to sub-regions. Parts of the object are detected in a selection of the sub-regions using a deep-learning neural network (DNN) model. Bounding boxes for the selection are also determined. The bounding boxes are evaluated based on a confidence score detected as above a threshold level. The confidence score designates the parts as contained within the selection. Next, a region of interest (ROI) is determined as a group including the selection. Similar orientations associated with the bounding boxes are comparable to similar orientations of a positive training model of the DNN model. Furthermore, the selection is designated as the ROI within the medical image. The medical image is provided with the ROI to a user.

What is claimed is: 
     
         1 . A method comprising:
 receiving, by a processor, an image;   determining, by the processor, a size of the image;   utilizing, by the processor, a neural network (NN) model to analyze the image to identify objects within the image based at least in part on confidence score; and   instructing, by the processor, an output computer module to produce an alert indicative of the identified objects.