![](bg2.png)
has been an increasing interest in the development
of automated analysis software using computer ma-
chine learning/artificial intelligence (AI) for analysis
of retinal images in people with diabetes thus solving
at least some part of the problem (Zhuang and Ette-
hadi, 2020). Second, some of these patients live in
remote locations and are unable to see an eye doctor.
Third, as a result, follow-up is essential.
For years, the attitude and/or behavioral traits
have been a source of contention despite awareness,
have a detrimental influence on the patients’ practice
of repercussions Provision can help to resolve these
challenges (Sharif, 2020). The presence of an auto-
mated imaging device within easy reach of patients
(Abed et al., 1999). As a result, there has been a surge
of interest in the computer-assisted creation of auto-
mated analytical software. For the analysis of data,
machine learning or artificial intelligence (AI) is used
to retinal images in diabetics, therefore resolving at
least a portion of the issue.
2 PREVIOUS WORK
Proposed classification of the grade of non-
proliferative diabetic retinopathy at any retinal
image. In order to extract features, used by a support
vector machine, an initial image processing stage
isolates micro aneurysms, blood vessels and hard
exudates. To figure out the retinopathy grade of
each retinal image used by a support vector machine.
The evaluation was implemented in the software
MatlabR. Getting better results from the support
vector machine (SVM) than other machine learning
algorithms (Carrera et al., 2017).
Proposed that the analysis which used the deep
CNN method is extremely particular during an auto-
mated DR severity grading and also very useful dur-
ing a large-scale database. Feature enhancing meth-
ods like matched filtering and region growing are
combined with SVM and Neural networks and are uti-
lized for various classification problems of DR sever-
ity. There has been growth of Graphics Processing
Units (GPUs) because of the growth of a Deep CNN
based feature extraction method. As a result, deep
learning models for DR detection are popular among
several researchers. Deep learning ensures high accu-
racy and performance and is a powerful tool for DR
detection (Rajesh et al., 2022).
In their paper used a CNN method with a bi-
nary tree-based ensemble of classifiers to increase the
performance. The model trained could provide us-
able point-of-care diagnostic services for DR. The
model was so well equipped that it could even be used
with other medical applications. The developed ap-
plication could only diagnose images from a phone’s
gallery (Hagos et al., 9 01).
Another study, proposed a multi-categorical dis-
ease detector system by utilizing deep learning tech-
niques. This study consisted of STARE (structured
analysis of the retina) database and detected differ-
entretina features like lesion, optic disk, and blood
vessels. Thus, CNN was utilized for disease classi-
fication. The augmentation and normalization of im-
ages have been done to enhance the number of im-
ages to prevent the overfitting issue. The blood ves-
sels were extracted using the morphological dilation,
erosion, adaptive histogram equalization (AHE), and
CLAHE. The optic disk removal was done with the
aid of the Canny edge detector and thresholding of
the images. The exudates appear due to leakage of
blood vessels because of hypertension, diabetes, and
vein obstruction. So, these yellowish exudates were
mainly detected with Gaussian blurring and binary
thresholding. The classification stage encompassed
two main stages, training and testing stages. The
training phase consisted of determining the represen-
tative classes and attributes from the training dataset.
Further, the dataset was associated with the class to
which it bears a resemblance; then the network deter-
mined the disease category of the conforming image
(Rajan and Sreejith, 2018).
Proposed an automated system using a convo-
lutional neural network (CNN) to detect diabetic
retinopathy and its severity stage. The model was
trained on approximately 88000 labeled retinal pho-
tographs, and a quadratic weighted-kappa metric was
used to determine the classification efficiency. The
proposed system acquired 82% accuracy in detecting
the DR, 51% for assessing its current stage and a de-
cent kappa value of 0.776. Results depicted that the
effectiveness of the neural networkcan be used for a
complex medical problem (Kwasigroch et al., 2018).
3 METHODOLOGY
The proposed method is developed and presented
here. The flowchart of the processes is illustrated in
figure 1. All the processes involved is explained in
this section.
Figure 1: Methodology.
ICAISD 2023 - International Conference on Advanced Information Scientific Development
188