The document presents a conference presentation on early detection of blindness due to diabetic retinopathy (DR) using ensemble learning techniques on retinal images. It discusses the prevalence of diabetes and DR, the challenges of early diagnosis, and proposes a machine learning methodology that employs retinal image analysis for improved detection. The study utilizes a dataset from the Asia Pacific Tele-Ophthalmology Society and outlines future research directions to enhance the classification model's performance.

1. Early Blindness Detection Based on Retinal Images Using
Ensemble Learning
[Conference Presentation]
Contributing Authors
Niloy Sikder
M.Sc. Student
CSE Discipline
Khulna University, Khulna
niloysikder333@gmail.com
Md. Sanaullah Chowdhury
B.Sc. Student
ECE Discipline
Khulna University, Khulna
sanaullahashfat@gmail.com
Dr. Abdullah Al Nahid
Associate Professor
ECE Discipline
Khulna University, Khulna
nahid.ece.ku@gmail.com
Abu Shamim Mohammad Arif
Professor
CSE Discipline
Khulna University, Khulna
shamimarif@yahoo.com
Presenter
Md. Sanaullah Chowdhury

2. Dec 20, 2019 1
Diabetic Retinopathy (DR)
Fig. 1: Blood vessels surrounding a normal
retina, and a retina with DR[1]
 A medical condition where the retina is affected by Diabetes.
 Diabetes vandalizes the blood vessels within the retinal tissue, causes them to
leak blood, fluids, and lipids inside the macula; and blocks them completely.
 It may also caused by the formation on new (uncontrolled)
blood vessels on the retina.
 Because of these disruptions, light can not be projected
properly on the retinal surface to create the sense of vision.
 Symptoms may not be apparent at the early stages making it
hard to diagnose early.
 DR can cause permanent vision loss.

3. Dec 20, 2019 2
DR Effects
Fig. 2: (a) Normal vision, (b, c, d) vision of a person with DR[2]
 A person with DR may experience blurred vision, floaters, blank areas or glare
because of the retinal damage.
(b)
(a)
(d)
(c)

4. 3
Prevalence of Diabetes and DR
 In 2015, 415 million people (worldwide) had some level of diabetes; and the
number will rise to an overwhelming 552 million by 2030.
 A 2013 study shows that in Bangladesh, the prevalence rate of retinal damage
among diabetic patients was 21.6%.
 In USA, 4.2 million adults has DR and 655,000 had vision-threatening DR.
 Male gender, higher A1c level, longer duration of diabetes, insulin use, and
higher systolic blood pressure are associated with the presence of DR.
Dec 20, 2019

5. 4
Prevalence of Diabetes and DR (cont.)
Fig. 3: Diabetes statistics Fig. 4: DR statistics
Dec 20, 2019

6. 5
DR Diagnosis
Fig. 5: (a) A Fundus Photography machine, and image of a retinal with (b) no DR, (c) prolific DR
 Checking the eyes regularly is the only way to diagnose DR early.
 The most common method of diagnosis is based on analyzing the retinal
images collected using Fundus Photography.
(a) (b) (c)
Dec 20, 2019

7. Dec 18, 2019 6
Why Use Machine Learning (ML) in the Diagnosis of DR?
 To ensure rapid/ periodic eye examination.
 To provide an easy way to check the health of the eyes so that people do not
hesitate to check every once in a while.
 Especially helpful to the people of poor economy.
 To save time and money.
 To lower the rate of vision loss due to prolific DR.

8. Dec 20, 2019
7
DR Dataset
Fig. 6: DR classes in the employed dataset
 In this study, the retinal images of the Asia Pacific Tele-Ophthalmology Society
2019 Blindness Detection (APTOS 2019 BD) dataset were used.
 The dataset contains 3,662 retinal images collected from numerous subjects
living in the rural areas of India.
 The images were put together by Aravind Eye Hospital, India, with the purpose
of building an ML model to detect blindness without medical screening.
 Based on the severity, the images are categorized as – no DR, mild DR,
moderate DR, severe DR, and proliferative DR.

9. Dec 20, 2019 8
Proposed Methodology
Fig. 7: Proposed methodology for retinal image classification.
Image Preparation
Image Preprocessing
Retinal
Image
Collection
Image
Crop
Image
Resize
Tone
Mapping
Image
Histogram
Noisy
Image
Exclusion
Operational
Dataset
Preparation
Train-Test
Split
(70 – 30)
Training
Data
Training
Labels
Test
Data
Classification
Model
ET
Hypertuning
Predicted
Labels
Image
Classification
Image
Augmentation
(Class Specific)
Removing
Black
Corners
Image Classification
Feature Extraction

10. Dec 20, 2019 9
Exclusion of Noisy Images
Fig. 8: A few of the images excluded from the study
 602 images were excluded form the study since they were out of focus,
overexposed, underexposed, or containing artifacts.

11. Dec 20, 2019 10
Image Crop Operation
Fig. 9: (a) A sample image where the retinal has been captured entirely, (b) the targeted portion of (a), (c) a
sample image where the retinal has been captured partially, and (d) the targeted portion of (c).
 Images of the APTOS 2019 BD dataset were not captured in the same
condition using the same equipment.
 The resultant images contain portions of the retina in different alignments.
 For blindness detection, the features of the reddish retina are required, not the
black border around it.

12. Dec 20, 2019 11
Tone Mapping Images
Fig. 10: (a) A sample retinal image, (b) its tone-mapped view, and (c) areas that
do not contain necessary information
 Tone mapping a digital image processing technique to convert a High Dynamic
Range (HDR) image to a Low Dynamic Range (LDR) image.
 Tone mapping is done by compressing the dynamic range of the HDR image
while preserving the details of the image.
 The common forms are linear scaling, logarithmic mapping, and exponential mapping.
Fig. 11: Histogram of 10 (c)

13. Classification Using ExtraTree Classifier
12

14. Scopes for Future Studies
 Include the Omitted images and extract features from them
 Devise a plan to deal with the noisy labels
 Hyper-tune the model to provide better classification performance
 Work on to develop a new model that will provide better performance
 Include various feature engineering techniques for feature reduction
Dec 20, 2019 13

15. 14
References
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Dec 20, 2019

16. Thank You
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