Automatic graph scoring for neuropsychological assessments

A system comprising a server, the server comprising a hardware computing device coupled to a network and including at least one processor executing within a memory instructions which, when executed, cause the system to:

• receive an image input comprising a digital image data;
• modify the digital image data to reduce a width of at least one feature within the digital image data, wherein the instructions further cause the system, for each of a first plurality of pixels within the digital image data to:
• identify a pixel within the first plurality of pixels;
• identify a total number of black pixels within:
• a second plurality of pixels immediately adjacent to the pixel;
• and
• a third plurality of pixels immediately adjacent to the second plurality of pixels;
• determine whether the total number of black pixels is greater than a threshold number;
• responsive to a determination that the total number of black pixels is greater than the threshold number, characterize the pixel as a black pixel;
• responsive to a determination that the total number of black pixels is not greater than the threshold number, characterize the pixel as a white or an empty pixel;
• execute a dimension reduction process on the at least one feature within the digital image data;
• classify the digital image data as correct or incorrect according to a classification software engine;
• and
• generate an output labeling the digital image data as correct or incorrect.

A method comprising the steps of:

• receiving, by a server comprising a hardware computing device coupled to a network and comprising at least one processor executing computer-executable instructions within a memory, a plurality of image inputs, each including a digital image data;
• modifying, by the server hardware computing device, the digital image data to reduce a width of at least one feature within the digital image data, further comprising the steps of, for each of a first plurality of pixels within the digital image data:
• Identifying, by the server hardware computing device, a pixel within the first plurality of pixels;
• Identifying, by the server hardware computing device, a total number of black pixels within:
• a second plurality of pixels immediately adjacent to the pixel;
• and
• a third plurality of pixels immediately adjacent to the second plurality of pixels;
• determining, by the server hardware computing device, whether the total number of black pixels is greater than a threshold number;
• responsive to a determination that the total number of black pixels is greater than the threshold number, characterizing, by the server hardware computing device, the pixel as a black pixel;
• responsive to a determination that the total number of black pixels is greater than the threshold number, characterizing, by the server hardware computing device, the pixel as a white or an empty pixel;
• executing, by the server hardware computing device, a dimension reduction process on the at least one feature within the digital image data;
• storing, by the server hardware computing device, within a data store:
• a feature vector for each digital image data in the plurality of transmissions, the feature vector comprising the at least one feature of the digital image data;
• and
• a correct or incorrect label associated with each feature vector;
• selecting, by the server hardware computing device, from the data store, the feature vector for each digital image data;
• training, by the server hardware computing device, a classification software engine to classify each feature vector according to the label associated with the feature vector.