Abstract
A Diabetic eye infection is one of the serious issues around the world. That can cause major impairment to the
eyes, including a permanent loss of vision. Early detection of eye diseases increase the survival rate by
successful treatment. The proposed methodology is to explore machine learning technique to detect diabetic
diseased using thermography images of an eye and to introduce the effect of thermal variation of abnormality
in the eye structure as a diagnosis imaging modality which are useful for ophthalmologists to do the clinical
diagnosis. Thermal images are pre-processed, and then Gray Level Cooccurrence Matrix (GLCM) based texture
features from gray images, statistical features from RGB and HSI images are extracted and classified using
classifier with various combination of features. A statistical method of examining texture that considers the
spatial relationship of pixels is the gray-level co-occurrence matrix (GLCM), also known as the gray-level spatial
dependence matrix. RGB is the most widely used color space , and we have effectively talked about it in the past
instructional exercises. RGB represents red green and blue. What RGB model states , that each shading picture
is really shaped of three unique pictures. Red picture , Blue picture , and dark picture. A normal grayscale image
can be defined by only one matrix, but a color image is actually composed of three The HSI color model
represents every color with three components: hue (H), saturation (S), intensity (I). different matrices.
Keywords: Fundus images, Fundus photography, Blood vessels extraction, Wavelet transform, Gabor filter,
Diabetic retinopathy.
Modules
Algorithms
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL