Investigations of CNN for Medical Image Analysis for Illness Prediction
Table 10
A comprehensive view of classification and preprocessing in a DCNN with the proposed GMPR-PReLU along with the efficacies of GLCM, AHE, and CLAHERD in the proposed DCNN.
GLCM with GMPR-PReLU
AHE with GMPR-PReLU
CLAHERD with GMPR-PReLU
Image is converted to gray-scale image
Image is converted to HSV array
Image is not disturbed and its contrast values of RGB values for relevant colors are extracted
Light colors of pixels are confused with other symptoms
Value of pixel does not signify the exact contrast of the required pixels
Required objects of the images are extracted exactly, since colors codes are applied
Ambiguity of identifying exudates
Color loss due to high intensity of value
Color is intact, and objects are selected
Two-value histogram is drawn and does not signify the existence of exudate symptoms
Full-color histogram is drawn, difficult to distinguish the objects with exudates
As only objects with exudate are developed, histogram signifies the intensity of exudates
Not possible to distinguish objects
Possible distinction of objects with much aberration
Objects are distinguished with very slight aberration
Total population: 89; samples: 35; average samples size: 40.