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.



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