Deep Learning Assisted Plus Disease Screening of Retinal Image of

Infants

Vijay Kumar

, Vatsal Agrawal

, Shorya Azad

and Kolin Paul

1, 2

Khosla School of Information Technology, Indian Institute of Technology, Delhi, India

Department of Computer Science and Engineering, Indian Institute of Technology, Delhi, India

Keywords:

Fundus Image, Retinopathy of Prematurity (ROP), Plus Disease, Generative Adversarial Network (GAN),

U-Net, Blood Vessels Segmentation, Deep Learning (DL), Vessel Tortuosity.

Abstract:

Retinopathy of Prematurity (ROP) is the leading cause of blindness in premature infants in developing coun-

tries. The international classiﬁcation of ROP (ICROP) classiﬁes ROP based on location, severity, and stage.

Plus disease is a crucial feature in ROP classiﬁcation. Plus is the most severe form of vascular dilatation

and tortuosity, and it causes severe ROP and visual loss if untreated. Despite decades of research, identify-

ing and quantifying Plus diseases is challenging. Understanding and detecting Plus in ROP patients can help

ophthalmologists provide better treatment, restoring vision to many infants with severe ROP. Hence, we have

proposed a robust Deep Learning-assisted framework for Blood Vessels map generation and analysis that may

effectively address the issue related to Plus disease screening and monitoring. We have extensively studied

various methods for computing and locating different blood vessel map features such as vessel branch point,

vessel width, vessel skeleton/centre-line, vessel segment tortuosity, etc. Additionally, we divided the branches

into two levels based on the width of the branches. For our investigations, we have used both local and public

databases. This work also includes a detailed analysis of these datasets’ vascular feature and their level.

To the best of our knowledge, none of the publicly available models could independently classify branches

and/or analyse the tortuousness based on the parent and child relationship of branches. For Plus, pre-Plus,

and Healthy infants, the average tortuosity index is 1.959, 1.1530, and 1.126, and the percentage of vessels

severely infected is 44%, 30%, and 20%, respectively. Moreover, our algorithm recognises and analyses many

vessels. The precision of many parameters is remarkable.

1 INTRODUCTION

Retinopathy of prematurity (ROP) is one of the most

common causes of retinal disease in preterm new-

borns across the world (Organization et al., 2019).

The International Classiﬁcation of ROP (ICROP)

classiﬁes ROP into three zones, and ﬁve stages based

on its anteroposterior position (area), severity (stage),

and vascular features (disease severity) (Chiang et al.,

2021). Plus disease is another condition in which the

central and posterior retinal blood vessels become di-

lated and tortuous, as shown in Figure 1. In the ROP

disease classiﬁcation system, Plus disease is a key

factor (Chiang et al., 2021). Plus disease causes per-

manent vision loss if left untreated. Additionally, the

neonatal care division and ophthalmologists have lim-

ited time to provide appropriate care, and prognosis

for these patients, which further worsens the condi-

tion and leads newborns to be at risk of ROP-induced

(a) ROP Zone-2 with Plus. (b) Plus disease symptoms.

Figure 1: Example infant’s retinal images with ROP-plus

disease.

visual impairment (Tian et al., 2019).

Classiﬁcation of Plus disease is mostly based on

the tortuosity and dilatation of blood vessels (Tian

et al., 2019). Tortuosity refers to the curvature of

arteries and veins. Despite decades of scientiﬁc re-

search, it remains difﬁcult to identify and quantify

vascular abnormalities (i.e., tortuosity and dilation)

accurately for Plus disease (Tian et al., 2019). Under-

538

Kumar, V., Agrawal, V., Azad, S. and Paul, K.

Deep Learning Assisted Plus Disease Screening of Retinal Image of Infants.

DOI: 10.5220/0011803700003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF, pages 538-545

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

standing vascular phenomena in ROP patients could

lead to the development of alternative treatment op-

tions that could restore the vision of a substantial

number of severely affected ROP patients. Therefore,

vessel analysis may be an effective strategy for ad-

dressing this problem.

Given its importance, authors have developed sev-

eral Plus disease diagnostic and classiﬁcation algo-

rithms using image processing, computer vision, and

machine learning (ML) or deep learning (DL). In the

last few years, ML/DL algorithms have signiﬁcantly

improved the performance of Plus disease diagnostic

and classiﬁcation algorithms (Reid and Eaton, 2019;

Tian et al., 2019). Although DL-based methods can

accurately detect and classify Plus, they do not pro-

vide a comprehensive and quantitative understanding

of the disease. As a result, ophthalmologists often

cannot link the results of the DL-based approach to

clinical symptoms. Furthermore, DL-based systems

are data-driven and require a large amount of labeled

pathological data for training, testing, and validation.

In the case of ROP disease, acquiring many fundus

images of infants is difﬁcult due to the lack of pub-

licly available datasets on the disease (Scruggs et al.,

2020). As a result, the advancement and usage of DL-

based methods in clinical use are restricted (Razzak

et al., 2018).

Therefore, this paper presents a solution for pro-

viding interpretable results, which can be used in clin-

ical settings by utilising a DL-assisted system that can

clearly detect and classify Plus disease by analysing

the blood vessels’ tortuosity.

The remainder of the paper is organized as fol-

lows. Section 2 highlights related studies on ROP-

Plus disease screening and monitoring. Section 3 de-

scribes the proposed DL-assisted approach for ROP-

Plus disease screening in detail. Section 4 provides

results of various pipeline stages of the proposed tech-

nique. Finally, Section 5 concludes the papers and

discusses how the work can be extended in the future.

2 RELATED WORK

Over the years, ophthalmologists have adopted many

ROP-Plus screening and diagnostic procedures. In

manual screening, an ophthalmologist uses a retinal

image to examine the signs of ROP disease. This is an

inefﬁcient and biased method (Reid and Eaton, 2019;

Ting et al., 2019). As a result, objective evaluation

is important. Several approaches assist the physician

and ophthalmologist with computer-assistive diagno-

sis (CAD) using various disease screening tools (Doi,

2007). For instance, (Oloumi et al., 2016) ex-

plored CAD to establish some conventional ways to

aid physicians in making objective detection. In ad-

dition, the fact that standard methods (based on im-

age processing and computer vision) can only extract

basic information from fundus pictures, which is in-

sufﬁcient to increase the accuracy of neonatal retinal

image classiﬁcation (Razzak et al., 2018). When light

is uneven, the contrast between the lesion and the sur-

rounding typical region is negligible, making fundus

images challenging to interpret and lesion character-

istics to be obscured (Kumar. et al., 2022). There-

fore, an ML and particularly DL-based technique is

required to extract in-depth, high-level information

that enables automated ROP-Plus screening.

The self-learning capability of DL and its efﬁ-

ciency and accuracy have attracted researchers’ in-

terest (Scruggs et al., 2020; Ting et al., 2019). Its

applications in ophthalmology have proven success-

ful, particularly in detecting image features of the

retina associated with glaucoma, age-related macu-

lar degeneration (AMD), diabetic retinopathy (DR),

cataract, and ROP (Ting et al., 2019). These are

data-driven approaches in which the DL model is

pre-trained on similar pathological datasets associ-

ated with the disease. DL-based solutions sur-

pass traditional CAD applications in terms of perfor-

mance. The DL-based screening method overcomes

the rule-based system’s ﬂexibility and adaptability

constraints (Razzak et al., 2018).

For training, testing, and validation, DL-based

systems require a large amount of labelled and un-

labelled data (Ting et al., 2019; Razzak et al., 2018).

Data collection (pathological, medication, and treat-

ment history) and labelling are time-consuming pro-

cedures for medical applications (Razzak et al., 2018;

Ting et al., 2019). In some contexts, a disease’s occur-

rence is restricted, and its features are inﬂuenced by

socioeconomic factors and regional boundaries, mak-

ing it challenging to obtain high-quality critical in-

formation. In (Ting et al., 2019), the authors intro-

duced DCNN-based reinforcement learning methods

for ROP that alleviate the developer’s/researcher’s ad-

ditional burden of labelling the DL model prior to

training. Whereas the trained deep learning model is

fast and effective on a particular dataset, its perfor-

mance on any other dataset is uncertain. Addition-

ally, it is challenging for an expert to understand and

describe the relationship between the clinical mani-

festations of the disease and the DL result (Scruggs

et al., 2020).

Therefore, we presents a DL-assisted ROP screen-

ing technique to address these issues where large his-

torical datasets are lacking and interpretation of DL-

based system results is required.

Deep Learning Assisted Plus Disease Screening of Retinal Image of Infants

539

Figure 2: DL assisted fundus image feature extraction and Plus disease screening.

3 SYSTEM ARCHITECTURE

The detailed architecture of the proposed system is

shown in Figure 2. It consists of four functional units:

• Fundus imaging (or retinal scanning) unit.

• Image pre-processing and retinal feature extrac-

tion .

• Post-processing and vascular feature extraction.

• Disease classiﬁcation.

3.1 Neonatal Fundus Imaging and Data

Preparation

The ophthalmologist or clinician acquired retinal im-

ages using the RetCam-3 fundus camera. RetCam-3’s

image resolution is 1600 x 1200 pixels. An infant’s

eye examination requires around ten photographs.

Figure 3 shows images of the retinas of premature

infants. Neonatal ophthalmologists use these images

in the diagnosis and monitoring of ROP as well as

other diseases. As, the quality of the picture is often

inadequate (because of uneven lighting, blurry mo-

tion, wrong set-up device, etc.) it is difﬁcult and

time-consuming to detect and follow the patholog-

ical changes induced by newborns. Therefore, we

used image reconstruction and enhancement methods

Table 1: Retinal image datasets used for training and testing

of OD, blood vessel segmentation and Plus disease classiﬁ-

cation modules (Here, NA: not available).

Dataset

Name

Number of im-

ages (in OD)

Number of im-

ages (in vessels

segmentation)

Number

of images

(Plus)

ARIA N/A 143 N/A

DRHAGIS N/A 40 N/A

STARE 297 20 N/A

FIRE 224 N/A N/A

DRIVE 40 20 N/A

HRF 45 45 N/A

IDRiD 511 N/A N/A

AIIMS 439 6 395

Total 1556 274 395

Table 2: AIIMS Dataset with 3-class Plus classiﬁcation.

no_Plus pre_Plus Plus Total

73 109 213 395

to improve quality and reduce the noise of scanned

images, discussed in Section 3.2.1.

In order to train, test, and validate the proposed

system, especially the DL-based models, a large

amount of historical data is required. Therefore, we

created different sets of fundus datasets for training,

testing, and validation of different modules as per the

availability of relevant historical data points ( or im-

ages).

As shown in Table 1, we labelled 1556 fundus

pictures for the OD model. On the other hand, we

used 274 images for training and testing the vessel

segmentation module. In addition, to test the effec-

tiveness of the segmentation model, we annotated the

blood vessels in an image of a preterm infant’s retina

as ground truth. The process of vessel annotation is

time-consuming and laborious. Hence, six blood ves-

sels have been annotated in each image.

For the ROP disease, no fundus image datasets are

available publicly. Hence, In order to build the ROP-

Plus screening and classiﬁcation system, we have use

439 images from the AIIMS dataset for ROP dis-

ease (Kumar et al., 2021; Kumar et al., 2022). Fur-

ther, a neonatal ophthalmologist classiﬁed and labeled

these images into three groups based on the severity

of Plus disease: Plus, Pre-Plus and Normal. Table 2

summarizes the overall number of Plus, Pre-Plus and

No-PLus preterm infants’ retinal scan images in the

AIIMS dataset.

We used datasets in two phases for the suggested

system of this research. In the ﬁrst phase, we used it

for training and testing extraction models for retinal

features (OD and blood vessel map), and in the sec-

ond, we used it for training and testing to classify the

image as Plus, Pre-Plus, and Healthy.

HEALTHINF 2023 - 16th International Conference on Health Informatics

540

(a) (b) (c)

Figure 3: Example images from the retinal vessel segmen-

tation and OD datasets. Left to right: (a) DRIVE: retinal

image of elderly people that are publicly available; (b) AI-

IMS image: retinal images of newborns’ healthy eyes with

no-ROP and no-Plus disease; and (c) AIIMS image: retinal

images of infants’ unhealthy eye with +ve ROP Zone-2 and

+ve Plus disease.

3.2 Image-Preprocessing and Retinal

Feature Extraction

3.2.1 Image-Preprocessing

This section describes the image pre-processing

methods used by the proposed system to reduce the

amount of noise in retinal scans. The neonatal’s fun-

dus image is pale yellowish. We preferred the G-

channel and used it in subsequent image-processing

modules (Kumar et al., 2021; Kumar. et al., 2022;

Kumar et al., 2022). We utilized a median, average

ﬁlter, and contrast-limited adaptive histogram equal-

ization (CLAHE) to enhance the quality of the colour

image and eliminate illumination variations and blur-

riness (Kumar et al., 2021). However, the colour im-

age is employed as an input in the DL system for fea-

ture extraction, disease diagnosis, and classiﬁcation.

3.2.2 Retinal Feature Extraction

In this section, we discuss the DL-based image analy-

sis methods used to extract retinal image feature’s OD

and blood vessels map.

OD Detection: The accuracy of a Plus classiﬁca-

tion is governed by the fact that how well the OD lo-

cation is recognised (because the reference point re-

quired for the vessel tracing is started from the opti-

cal nerve boundary in retinal images) and how well

the vessel segments are detected by tracing. For the

OD detection, we have used (Kumar et al., 2021). In

which, authors used pre-trained DL-models, YOLO-

v5 (Jocher et al., 2021) for OD detection. Further, la-

belled the compiled dataset as per the YOLO model’s

input and then trained the network with a 416x416 in-

put pixel resolution, summarised in Table 1.

Blood Vessels Segmentation: The retinal blood

vessel map is the most signiﬁcant retinal feature

used for tortuosity analysis in the proposed system.

Blood vessels of the preterm infant retina are not

distinguishable from the background as preterm in-

fants’ retinal blood vessel systems are not fully de-

veloped at the time of birth, as shown in Figure 3.

Therefore, conventional vessel segmentation meth-

ods, which perform well with publicly available reti-

nal image datasets, do not work well with an in-

fant’s retinal image. Hence, for vessels segmentation,

a Pix2Pix-GAN (Isola et al., 2018) or U-Net (Ron-

neberger et al., 2015) architecture-based vessel seg-

mentation method is used (Kumar. et al., 2022). Fur-

ther, we have use same approach for training and test-

ing as proposed in (Kumar. et al., 2022) and used

publicly available image datasets listed in Table 1.

3.3 Vascular Feature Extraction

To understand and quantify the ROP-Plus disease pro-

gression and severity, vascular features analysis is re-

quired. This includes analysis of blood vessels’ tor-

tuosity which requires the vessel’s skeleton, branch

points, width, length, and blood vessels end points.

For the vascular feature extraction, we have use

image-processing and computer vision based method

is shown in Figure 4. In this ﬂow chart, it performs

the following steps to compute blood vessel (BV) fea-

tures (which includes BV segment curve length (S),

chord length (D), and order of BV segments(O)):

1. Centreline ﬁnding

2. Noise removal followed by branchpoint and end

point detection.

3. Branch points removal and BV segmentation.

4. Segment length (or curve length) and Segment

chord calculation.

5. Tortuosity Calculation.

6. Ordering of blood vessels segments based on

blood vessels width.

Centreline Finding. Centreline can be found with

the help of a technique known as skeletonization.

Skeletonization is morphological operation. Matlab

support many skeletonization of a 2D binary image

function includes "bwskel", "skel" and "thin". In gen-

eral, it is found that erroneous branch points are dis-

covered due to one-time skeletonization, as it is possi-

ble for a point that is not a branch point to have three

pixels to be adjacent to each other in the condition of

highly tortuous arteries. So, we have used ’thin’ for

the removal of short-end branches and spurs as it may

also include unnecessary extra pixels in the curved re-

gion. We then used ’bwskel’ for centreline due to its

accuracy.

Deep Learning Assisted Plus Disease Screening of Retinal Image of Infants

541

Figure 4: Blood vessels feature extraction.

Figure 5: Illustration of segment (PQ) is used to estimate

blood vessel tortuosity by examining segment length (s)

with respect to its chord length (d).

Branch Point and end Point Detection. In this

stage, the branch-points and endpoints of the blood

vessel network are identiﬁed. The tortuosity level

must be measured, analyzed, and quantiﬁed by lo-

cating the branch-points of a skeletonized vascular

network. Branch points are recognized via a mor-

phological process that identiﬁes them, dilates them,

and reserves the branch point pixels that split various

segments (Wang et al., 2021; Sukkaew et al., 2008).

Therefore, we used Matlab’s morphological operation

to the binary image centerline with a simple kernel to

detect branch points with an adjacency of central pix-

els greater than 2 (MathWorks, 2023). After segmen-

tation, vessel segments are used for a tortuous assess-

ment.

Segment Length and Chord Length Calculation.

Vessel segment’s attributes segment length (S) and

chord length (D) are used for tortuosity calculation

are deﬁne below:

• Segment length or Geodesic distance (S): The

maximum non-inﬁnity quasi-Euclidean distance

between any two endpoints of the segment’s cen-

terline skeleton (PQ as shown in Figure 5) is used

to determine the arc-length distance between the

start and endpoints of the segment, as follows:

S(γ(Q) − γ(P)) = v ∗ |Q − P|, (1)

where, v ≥ 0.

• Straight line distance (Chord): It is the straight-

line distance (Euclidean distance) between two

ends (PQ as shown in Figure 5) of the skeleton’s

centerline segments, as follows (Heneghan et al.,

2002):

Chord(D) =

′

− x)

+ (y

′

− y)

, (2)

where, (x, y) and (x

′

, y

′

) are the co-ordinate of seg-

mented blood vessel’s start and end point, P and

Q respectively.

Tortuosity Calculation. For the tortuosity calcula-

tion, we have used two approaches. The ﬁrst ap-

proach, proposed by (Hart et al., 1997), is the sim-

plest and most widely used measure for calculating

tortuosity. It computes the tortuosity of a vessel by

examining how long the curve is relative to its chord

length, as follows:

Tortuosity(τ

) =

Curve length(S)

Chord length (D)

− 1, (3)

where S is the arc length or curve length obtained by

counting all the points (or pixels) from the start of the

vessel to its end, and D is the length of the underlying

chord.

There was another kind of method described by

(Grisan et al., 2008). This technique works by ﬁrst

segmenting each vessel into n segments of constant-

sign curvature and then combining these segments

and their respective numbers to compute as follows:

Tortuosity(τ

) =

n − 1

∑

i=1

[

− 1], (4)

where S is the vessel arc length, s

is the subsegment

arc length, and d

is the subsegment chord length.

HEALTHINF 2023 - 16th International Conference on Health Informatics

542

Ordering of Segmented Branches. Further, for the

analysis of retinal vascular structure, we have used

two types of blood vessel (BV) segmentation and la-

belling methods: (1) the minor analysis model and

(2) the major analysis model. In the minor analysis

model, we simply break vessels at branch points. In

the major analysis model, we break vessels at branch

points while maintaining parent-child relations.

The ﬁrst is only to increment the number if its

value is smaller than the others. This approach is ap-

propriate for two to three-level categorization. The

second method involves increasing the number with-

out limits. This method is sometimes better for im-

ages with more than three levels, but the level thresh-

old must be set manually for each new image. Repeat

the above step at least four more times to get the ﬁ-

nal output, which can be further used by the tortuosity

calculation algorithm to get a detailed analysis of ves-

sels. A two-level system is good enough for images

with a lot of noise, and a three-level system is good

enough for regular fundus image. However, higher-

level systems sometimes suffer from a false loop is-

sue.

3.4 Disease Classiﬁcation

Disease classiﬁcation is the fourth and ﬁnal functional

unit. In the proposed system, we classiﬁed the sever-

ity of PLUS disease using two rule-based approaches.

Percentage of Infected Vessels: Computes the pro-

portion of vessel segments whose curvature index ex-

ceeds the threshold curvature index (T

). Here, T

is the mean tortuosity index of the vessel segments in

the healthy fundus image.

Cumulative Curvature Index: It estimates the cur-

vature index of vessel segments using equation ( 4),

treating all fundus image segments as sub-segments

of a segment.

After that, we utilized the ROP-Plus dataset to

compute these threshold values for Plus disease clas-

siﬁcation (i.e., healthy, pre-Plus, and Plus) using both

of the above mentioned approaches. Using a training

dataset (listed in Table 2), all threshold values were

computed. The remaining dataset is utilized to test

the suggested system.

4 RESULTS AND DISCUSSION

The proposed system and its different modules are im-

plemented and tested on a workstation with Intel(R)

Xeon(R) 40-Core CPU E5-2630 v4 @ 2.20GHz with

64 GB RAM and 8 GB NVIDIA GeForce GTX 1070

GPU. Further, we used online Matlab to post-process

the segmented blood vessel map. In this section, we

discuss the results of the proposed method for vascu-

lar feature analysis to detect and classify infant ROP-

Plus.

4.1 Retinal Feature Extraction

We have used YOLO-v5 and Pix2Pix-GAN Or UNet

models to extract retinal characteristics (i.e., OD and

retinal blood vessel nap). Both algorithms have also

shown excellent accuracy for OD detection and blood

vessel segmentation using the AIIMS infant’s fundus

image dataset. Figure 6 shows the results of OD de-

tection and blood vessel segmentation on images of

infant retinas.

4.2 Vessels Segmentation and Labelling

We have used the vascular feature extraction method,

discussed in Section 3.3. The proposed method solves

the problem of the false loop to a large extent and re-

duces false branches and small segment branches. We

have taken several images and calculated the number

of vessels correctly detected and the total number of

vessels correctly detected with respect to each model

for each image. Then for each model, we found out

the percentage of vessels detected correctly for each

image and averaged them to get the accuracy of our

models. As shown in Figure 7d, for minor analysis,

we have found that at least 95% of the vessel was de-

tected correctly in all test cases, with 5% having a

problem with the false loop. For major analysis, we

found that almost in all test cases, principal (or main)

branches are not disconnected and have an accuracy

(a) (b)

Figure 6: Example results of OD detection and blood seg-

mentation. (a) Original OD, (b) predicted OD, (c) orig-

inal blood vessels(BV) network, (d) predicted BV using

Pix2Pix-GAN, and (d) predicted BV using UNet.

Deep Learning Assisted Plus Disease Screening of Retinal Image of Infants

543

(a) (b)

Figure 7: Minor analysis output. (a) Retinal image of

healthy infant; (b) Blood vessels map after segmentation;

tuosity value of each vessel segments.

Table 3: Plus disease classiﬁcation results using tortuosity

analysis of infants retinal images (Here, N/A: not applica-

ble).

Parameters Reference Healthy Pre-

Plus

Score Overall 1.0894 1.126 1.1530 1.1967

Score 2nd level 1.0739 1.117 1.1530 1.1959

% of vessel in-

fected

25% 35% 38% 56%

% of vessel

severely in-

fected

18% 20% 30% 44%

Average score

(27 patient)

N/A 1.12 1.16 1.21%

Average % (27

patient)

N/A 30% 40% 55%

of around 94% overall while detecting and segment-

ing vessels. For ordering the vessel, the accuracy was

more than 90% at 2-level classiﬁcation. The accuracy

falls to 60% for 3-level classiﬁcation, which can be

increased with some modiﬁcation and changing the

threshold for each speciﬁc case. Also, only a few

images of the locally available datasets have three or

more levels.

4.3 Plus Disease Classiﬁcation

After training on a dataset of infant retinal images

(Table 2), we identiﬁed a tortuosity threshold (T

) of

1.15 for labelling a blood vessel segment as healthy or

infected. Also, using this T

(for infected vessels), we

found a percentage threshold of 35% for pre-plus and

50% for Plus. In addition, we measured the cumu-

lative range of tortuosity and found it to be between

1.13 and 1.19 for both the pre-Plus and Plus classes.

(a) τ = 1.0894. (b) τ = 1.967.

Figure 8: Plus disease classiﬁcation results: (from left to

right). (a) Healthy reference image use to ﬁnd tortuosity

threshold; (b) Plus disease; (c) Pre-Plus; and (d) Healthy.

Refer to Table 3 for results. The healthy image

(from infant retinal image datasets) above has a tortu-

osity of 1.126 overall, and 1.1178 at the 2

level, and

the percentage of vessels infected is 35% with 20%

vessels severely infected. The pre-Plus image shown

above in Figure 8 has a tortuosity of 1.1530, and

the percentage of vessels infected is 38%, with 30%

severely infected. In contrast, the Plus image has a

tortuosity of 1.1959 in the 2nd level and 1.1967 over-

all, with 56% of vessels infected and 44% severely in-

fected. In comparison with the healthy reference im-

age, it has a tortuosity of 1.0739 in the 2nd level, and

1.0894 overall, and the percentage of vessels infected

is 25%, with 18% vessels severely infected. Severely

infected vessels have tortuosity above 1.2, due to ves-

sels curvature (shown in Figure 8).

5 CONCLUSION

This study presents a proof of concept (POC) for a

newly proposed DL-assisted retinal disease screening

system. It investigated, developed, and implemented

a new classiﬁcation system based on the tortuosity of

retinal blood vessels to diagnose ROP-Plus disease.

The proposed method allows data-driven and rule-

based systems to coexist on a single platform. Conse-

quently, it can function effectively even without a suf-

ﬁcient number of datasets. In addition, in this method,

the doctor can view the classiﬁcation results and the

visualization of the detected vessels, including their

segments, length, and tortuosity, to visually compre-

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544

hend and conﬁrm the system’s decision.

In the current study, blood vessel segments that

form a loop or overlap were not considered when cal-

culating the curvature index of the vessels. In addi-

tion, the present method is trained and evaluated with

a small number of retinal image datasets for three lev-

els of ROP-Plus disease classiﬁcation. Therefore, in

the future, we may enhance the proposed techniques

to address overlapping problems with more retinal

image datasets and additional disease classiﬁcation

levels.

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