Device to enhance and present medical image using corrective mechanism

Abstract

A device to enhance and present a medical image using a corrective mechanism is described. An image analysis application executed by the device captures a digital copy of the medical image displayed on a display device. A flawed photography effect associated with the digital copy is identified by processing the digital copy. Next, the digital copy is enhanced based on the flawed photography effect. Furthermore, the enhanced digital copy can be processed with an artificial intelligence mechanism to generate an annotation. The annotation is associated with a cancer identification. In addition, the enhanced digital copy and the annotation are displayed.

1 - 19 . (canceled) 
     
     
         20 . A method comprising:
 receiving, by at least one processor, a first digital medical image;   identifying, by at least one processor, a flawed photography effect associated with the first digital medical image by processing the first digital medical image using a deep learning model;   enhancing, by at least one processor, the first digital medical image based on the flawed photography effect using the deep learning model;   processing, by at least one processor, the first digital medical image using the deep learning model to generate an annotation, wherein the annotation includes at least one of a suspicious label, a not suspicious label, or a follow-up label associated with a cancer identification; and   displaying, by at least one processor, the first digital medical image and the annotation overlaid on the first digital medical image.   
     
     
         21 . The method of  claim 20 , wherein the flawed photography effect comprises at least one error resulting from a capture or production of the first digital medical image. 
     
     
         22 . The method of  claim 21 , wherein the error comprises an orientation, image blur, a red-eye detection, a blur, a color balance, an exposure, a noise information, or a combination thereof. 
     
     
         23 . The method of  claim 20 , wherein the deep learning model comprises a classifier machine learning model trained to identify cancerous tissue represented in digital imagery. 
     
     
         24 . The method of  claim 20 , wherein the suspicious label represents an identification. of a malignant cancer. 
     
     
         25 . The method of  claim 20 , wherein the not suspicious label comprises represents an identification of a benign cancer. 
     
     
         26 . The method of  claim 20 , wherein the follow-up label comprises represents an identification of a potentially malignant cancer. 
     
     
         27 . A method comprising:
 receiving, by at least one processor, a first digital medical image;   identifying, by at least one processor, a flawed photography effect associated with the first digital medical image by processing the first digital medical image using a deep learning model;   enhancing, by at least one processor, the first digital medical image based on the flawed photography effect using the deep learning model;   processing, by at least one processor, the first digital medical image using the deep learning model to generate an annotation, wherein the annotation includes at least one classification of a region-of-interest (ROI) within the first digital medical image; and   displaying, by at least one processor, the first digital medical image and the annotation overlaid on the first digital medical image.   
     
     
         28 . The method of  claim 27 , wherein the flawed photography effect comprises at least one error resulting from a capture or production of the first digital medical image. 
     
     
         29 . The method of  claim 28 , wherein the error comprises an orientation, image blur, a red-eye detection, a blur, a color balance, an exposure, a noise information, or a combination thereof. 
     
     
         30 . The method of  claim 27 , wherein the deep learning model comprises a classifier machine learning model trained to identify cancerous tissue represented in digital imagery. 
     
     
         31 . The method of  claim 27 , wherein the suspicious label represents an identification of a malignant cancer. 
     
     
         32 . The method of  claim 27 , wherein the not suspicious label represents an identification of a benign cancer. 
     
     
         33 . The method of  claim 27 , wherein the follow-up label represents an identification of a potentially malignant cancer. 
     
     
         34 . A system comprising:
 at least one processor configured to execute instructions stored in a non-transitory storage medium, wherein the instructions cause the at least one processor to perform steps to:
 receive a first digital medical image; 
 identify a flawed photography effect associated with the first digital medical image by processing the first digital medical image using a deep learning model; 
 enhance the first digital medical image based on the flawed photography effect using the deep learning model; 
 process the first digital medical image using the deep learning model to generate an annotation, wherein the annotation includes at least one of a suspicious label, a not suspicious label, or a follow-up label associated with a cancer identification; and 
 display the first digital medical image and the annotation overlaid on the first digital medical image. 
   
     
     
         35 . The system of  claim 34 , wherein the flawed photography effect comprises at least one error resulting from a capture or production of the first digital medical image. 
     
     
         36 . The system of  claim 34 , wherein the deep learning model comprises a classifier machine learning model trained to identify cancerous tissue represented in digital imagery. 
     
     
         37 . The system of  claim 34 , wherein the suspicious label represents an identification of a malignant cancer. 
     
     
         38 . The system of  claim 34 , wherein the not suspicious label represents an identification of a benign cancer. 
     
     
         39 . The system of  claim 34 , wherein the follow-up label represents an identification of a potentially malignant cancer.