[0001] The present disclosure relates, in general, to a device for diagnosis of diseases, and
more specifically, relates to a device for capturing retinal images to diagnose ophthalmic
diseases.
BACKGROUND
[0002] Background description includes information that may be useful in understanding
the present disclosure. It is not an admission that any of the information provided herein is prior
art or relevant to the presently claimed disclosure, or that any publication specifically or
implicitly referenced is prior art.
[0003] In current scenario, everything is connected to one another with either the
smartphone or any other device. This affects the daily lives of people between the age group of
20-70 years, who are more reliable on technology. Some of the working-age population too relies
on technology to get things done at a faster pace. Due to these types of lifestyle, one can face
various health-related issues, one such issue includes vision loss. There can be various other
symptoms such as diabetes, hypertension, aging that can also cause complete blindness.
[0004] Current technology is exceptionally advanced that the physicians can examine the
insight of the body with the help of various imaging techniques. Biomedical image processing,
computer vision techniques and machine learning techniques play a significant role in noninvasive treatment, especially in the field of medicine. The images can be processed either
manually, semi-automatic or automatic for further analysis of the image. Retinal images play a
crucial part in identifying the problems in the body.
[0005] Retinopathy also known as retinal abnormalities can be done in order to detect early
signs of vision loss. Retinopathy means the disorders related to the retina. There are various
types of retinal abnormalities such as hypertensive retinopathy, diabetic retinopathy, retinopathy
of prematurity, age-related macular degeneration, central serous retinopathy. So, the fund us
images should be taken in order to analyses the retinal abnormalities. The huge amount of data
3
should be saved at the corresponding data center and the underlying network technology to
traverse this data should be innovative, dynamic, and software defined.
[0006] Currently, artificial intelligence (AI) system includes extensively validated AI
technology for autonomous detection of diabetic retinopathy, tested in the real-world on more
than half million patients and nearly two million retinal images globally. These system makes inclinic, real-time diabetic retinopathy (DR) screening possible for primary care practices, diabetes
centres and optometric offices by allowing physicians to quickly and accurately identify
referable DR patients during a diabetic patient’s regular exam. However, these systems only
focus on diabetic retinopathy screening.
[0007] Therefore, there is a need for a means to provide a flexible, and cost-effective
system that focus on all types of ophthalmic diseases.
OBJECTS OF THE PRESENT DISCLOSURE
[0008] An object of the present disclosure relates, in general, to a device for diagnosis of
diseases, and more specifically, relates to a device for capturing retinal images to diagnose
ophthalmic diseases.
[0009] Another object of the present disclosure is to provide a system that can perform
screening of all retinopathy diseases effectively and can easily identify people who are at risk of
vision loss.
[0010] Another object of the present disclosure is to provide a system that can enable the
use of secure SDN technology.
[0011] Another object of the present disclosure is to provide system in which the reports of
the user can be available in less time. The system is fully automated, flexible to provide image
quality feedback effectively.
[0012] Yet another object of the present disclosure is to provide a secure and cost-effective
system for detecting retinopathy.
SUMMARY
[0013] The present disclosure relates, in general, to a device for diagnosis of diseases, and
more specifically, relates to a device for capturing retinal images to diagnose ophthalmic
diseases.
4
[0014] In an aspect, the present disclosure provides a system for detecting retinopathy in
an eye of a subject, the system including: an image capturing device configured to obtain one or
more images of the eye of the subject; and a processor operatively coupled with a memory, said
memory storing instructions executable by the processor to: receive, from the image capturing
device, the one or more images of the eye of the subject; analyse the received one or more
images to extract a set of attributes from the one or more images; classify the extracted set of
attributes based on matching of the extracted set of attributes with a reference set of attributes,
the classification pertaining to a plurality of health attributes of the eye; and extract, from the
extracted set of attributes, a set of values for the extracted set of attributes, wherein, based on a
combination of classification of the extracted attributes and a deviation of the extracted set of
values for the extracted set of attributes from a reference set of values, the processor is
configured to determine a diagnosis for the eye of the subject, the diagnosis pertaining to state of
retinopathy of the eye.
[0015] In an embodiment, the determined diagnosis can be displayed on a display device
operatively coupled to the processor.
[0016] In another embodiment, the system includes a memory device operatively coupled
with the processor, the memory device configured to store a log of operations of the system, the
log of operations including any or a combination of the one or more images, the extracted set of
attributes, the reference set of attributes, the classification of the extracted set of attributes, the
extracted set of values for the extracted attributes, the reference set of values for the extracted
attributes and the determined diagnosis for the eye of the user.
[0017] In another embodiment, the memory device can be a cloud storage.
[0018] In another embodiment, the processor can be operatively coupled to a learning
engine, the learning engine trained to detect retinopathy of the eye of the user.
[0019] In another embodiment, the learning engine can be trained using a historical data of
correlation of the extracted set of attributes of a received one or more images of the eye with a
diagnosis for the eye, the diagnosis pertaining to a state of retinopathy of the eye.
[0020] In another embodiment, the learning engine includes any or a combination of
convolutional neural networks (CNN) and deep neural network (DNN).
5
[0021] In another embodiment, the system can be operated by software defined networking
(SDN) controller configured to support the underlying network and secure the data traversed by
multiple features of SDN.
[0022] In an aspect, the present disclosure provides a method for detecting retinopathy in
an eye of a subject, the method including: receiving, at a computing device from an image
capturing device, the one or more images of the eye of the subject; analysing, at the computing
device, the received one or more images to extract a set of attributes from the one or more
images; classifying, at the computing device, the extracted set of attributes based on matching of
the extracted set of attributes with a reference set of attributes, the classification pertaining to a
plurality of health attributes of the eye; and extracting, at the computing device from the
extracted set of attributes, a set of values for the extracted set of attributes, wherein, based on a
combination of classification of the extracted attributes and a deviation of the extracted set of
values for the extracted set of attributes from a reference set of values, the processor is
configured to determine a diagnosis for the eye of the subject, said diagnosis pertaining to state
of retinopathy of the eye.
[0023] Various objects, features, aspects, and advantages of the inventive subject matter
will become more apparent from the following detailed description of preferred embodiments,
along with the accompanying drawing figures in which like numerals represent like components.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The following drawings form part of the present specification and are included to
further illustrate aspects of the present disclosure. The disclosure may be better understood by
reference to the drawings in combination with the detailed description of the specific
embodiments presented herein.
[0025] FIG. 1 illustrates an exemplary representation of a system for detecting retinopathy
in an eye of a subject, in accordance with an embodiment of the present disclosure.
[0026] FIG. 2 illustrates a flow chart of the process for detecting retinopathy in an eye of a
subject, in accordance with an embodiment of the present disclosure.
[0027] FIG. 3 illustrates an exemplary computer system in which or with which
embodiments of the present invention can be utilized in accordance with embodiments of the
present disclosure.
6
DETAILED DESCRIPTION
[0028] The following is a detailed description of embodiments of the disclosure depicted in
the accompanying drawings. The embodiments are in such detail as to clearly communicate the
disclosure. However, the amount of detail offered is not intended to limit the anticipated
variations of embodiments; on the contrary, the intention is to cover all modifications,
equivalents, and alternatives falling within the spirit and scope of the present disclosure as
defined by the appended claims.
[0029] If the specification states a component or feature “may”, “can”, “could”, or
“might” be included or have a characteristic, that particular component or feature is not required
to be included or have the characteristic.
[0030] As used in the description herein and throughout the claims that follow, the
meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates
otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on”
unless the context clearly dictates otherwise.
[0031] The use of any and all examples, or exemplary language (e.g., “such as”) provided
with respect to certain embodiments herein is intended merely to better illuminate the invention
and does not pose a limitation on the scope of the invention otherwise claimed. No language in
the specification should be construed as indicating any non – claimed element essential to the
practice of the invention.
[0032] The present disclosure relates, in general, to a device for diagnosis of diseases, and
more specifically, relates to a device for capturing retinal images to diagnose ophthalmic
diseases.
[0033] In an aspect, the present disclosure provides a system for detecting retinopathy in
an eye of a subject, the system including: an image capturing device configured to obtain one or
more images of the eye of the subject; and a processor operatively coupled with a memory, said
memory storing instructions executable by the processor to: receive, from the image capturing
device, the one or more images of the eye of the subject; analyse the received one or more
images to extract a set of attributes from the one or more images; classify the extracted set of
attributes based on matching of the extracted set of attributes with a reference set of attributes,
the classification pertaining to a plurality of health attributes of the eye; and extract, from the
extracted set of attributes, a set of values for the extracted set of attributes, wherein, based on a
7
combination of classification of the extracted attributes and a deviation of the extracted set of
values for the extracted set of attributes from a reference set of values, the processor is
configured to determine a diagnosis for the eye of the subject, the diagnosis pertaining to state of
retinopathy of the eye.
[0034] In an embodiment, the determined diagnosis can be displayed on a display device
operatively coupled to the processor.
[0035] In another embodiment, the system includes a memory device operatively coupled
with the processor, the memory device configured to store a log of operations of the system, the
log of operations including any or a combination of the one or more images, the extracted set of
attributes, the reference set of attributes, the classification of the extracted set of attributes, the
extracted set of values for the extracted attributes, the reference set of values for the extracted
attributes and the determined diagnosis for the eye of the user.
[0036] In another embodiment, the memory device can be a cloud storage.
[0037] In another embodiment, the processor can be operatively coupled to a learning
engine, the learning engine trained to detect retinopathy of the eye of the user.
[0038] In another embodiment, the learning engine can be trained using a historical data of
correlation of the extracted set of attributes of a received one or more images of the eye with a
diagnosis for the eye, the diagnosis pertaining to a state of retinopathy of the eye.
[0039] In another embodiment, the learning engine includes any or a combination of
convolutional neural networks (CNN) and deep neural network (DNN).
[0040] In another embodiment, the system can be operated by software defined networking
(SDN) controller configured to support the underlying network and secure the data traversed by
multiple features of SDN.
[0041] In an aspect, the present disclosure provides a method for detecting retinopathy in
an eye of a subject, the method including: receiving, at a computing device from an image
capturing device, the one or more images of the eye of the subject; analysing, at the computing
device, the received one or more images to extract a set of attributes from the one or more
images; classifying, at the computing device, the extracted set of attributes based on matching of
the extracted set of attributes with a reference set of attributes, the classification pertaining to a
plurality of health attributes of the eye; and extracting, at the computing device from the
extracted set of attributes, a set of values for the extracted set of attributes, wherein, based on a
8
combination of classification of the extracted attributes and a deviation of the extracted set of
values for the extracted set of attributes from a reference set of values, the processor is
configured to determine a diagnosis for the eye of the subject, said diagnosis pertaining to state
of retinopathy of the eye.
[0042] FIG. 1 illustrates an exemplary representation of a system for detecting retinopathy
in an eye of a subject, in accordance with an embodiment of the present disclosure.
[0043] Referring to FIG. 1, a system 100 configured for detecting ophthalmic disease in
patients. The system 100 can provide automated image analysis of the medical image. The
system 100 can be used to conduct automated screening of patients with one or more diseases
e.g., retinal diseases. Software defined networking (SDN) 112 technology is utilized to support
the underlying network infrastructure of the system. The SDN enhances network performance
and monitoring.
[0044] In an embodiment, system 100 includes a fund us camera 102 (also referred to as
image capturing device 102, herein) that can capture images of interior parts of retina. The
fundus camera 102 can be deployed at a primary care facility. They capture images at varying
resolutions and field of view. It is used to take images of interior part of the retina that includes
macula, optic disc, blood vessels and abnormalities such as microaneurysms, hemorrhages,
exudates, tortuosity, bifurcation etc. Analysis of the fund us images can be done by applying
various steps.
[0045] In an embodiment, system 100 may include one or more processors 104. A memory
configured to store executable instructions configured for execution by the one or more
processors. The processor 104 can be operatively coupled to the fund us camera 102in the system
and can be configured to analyse the images e.g., pixel information for the input image or other
information characterizing the input image. The system 100 can perform computation of the fund
us images based on images taken at different time intervals, for example, approximately once
every year or about six months. The images of a patient's eye from different visits can be also coregistered.
[0046] In an embodiment, the images captured by the camera 102 are transmitted to the
processor 104. The image analysis can be performed by utilizing learning engine 114 e.g.,
convolutional neural networks (CNNs), deep neural network (DNN) and the like. The learning
engine 114 includes feature extraction unit 116, classification unit 118 and image enhancement
9
unit 120. The processor 104 can build and train an ensemble of learning engine 114 to accurately
and automatically perform image processing to detect particular attributes of objects in a digital
image, and to classify the objects according to the detected attributes. The determined diagnosis
can be displayed on a display device operatively coupled to the processor.
[0047] The learning engine 114 can be used by the processor 104 for extracting various
features from image e.g., pixel information for the input image or other information
characterizing the input image. The various features from image can be used for classifying
images, image enhancement and image grading. Furthermore, each component of the learning
engine 114 typically has a multitude of parameters associated with it. The processor can use
these learning engines 114 to output an accurate classification of an image.
[0048] In an embodiment, the input image can be analysed and can be used for diagnosing
the severity of the disease. Various process such as pre-processing, feature extraction, image
enhancement and classification followed by grading of images are applied to the image, wherein
the grading and specification of disease can be done by applying various parameters. The
parameters may include a learning rate, a batch size, a maximum number of training epochs, an
input image size, a number of feature maps at every layer of the CNN, a convolutional filter size,
a sub-sampling pool size, a number of hidden layers, a number of units in each hidden layer, a
selected classifier algorithm, and a number of output classes. For example, the processor can
receive input image data. The processor can process the received data using a deep neural
network and an output layer to generate an output for the input image.
[0049] In an embodiment, the processor 104 can be configured to analyse the received
images to extract a set of attributes from the one or more images. The extracted set of attributes
can be classified the based-on matching of the extracted set of attributes with a reference set of
attributes, the classification pertaining to the health attributes of the eye. A set of values can be
extracted for the extracted set of attributes, wherein, based on a combination of classification of
the extracted attributes and a deviation of the extracted set of values for the extracted set of
attributes from a reference set of values, the processor can be configured to determine a
diagnosis for the eye of the subject.
[0050] In an embodiment, the analysed and segregated images are stored on cloud server
110 (also referred to as memory device 110, herein), and the user can access the data anytime
from any place. The cloud server 110 can be operatively coupled with the processor 104. The
10
cloud server 110 can be configured to store a log of operations of the system, the log of
operations including any or a combination of the one or more images, the extracted set of
attributes, the reference set of attributes, the classification of the extracted set of attributes, the
extracted set of values for the extracted attributes, the reference set of values for the extracted
attributes and the determined diagnosis for the eye of the user.
[0051] Additionally, a web application 106 can be implemented to check the images and
related data, wherein data received from the camera 102 and stored into the database 108, which
further transmitted to the cloud server 110. The web application 106 can allow the user to
securely login and review images remotely across the globe. Further, software Defined
Networking (SDN) technology is used to support the underlying network infrastructure, wherein
overall network manageability can be achieved by SDN controller 112 and data traversed is
secured by utilizing multiple available features of SDN. SDN is a network architecture in which
network control plane and policies are decoupled from network infrastructure and client devices,
and placed in a logically centralized controller. SDN allows an administrator to orchestrate and
automate control of network services, such as network components and applications through
abstraction of a lower level functionality.
[0052] Thus, the system 100 enabling the use of innovative, cost effective and secure SDN,
technology. SDN allows the administrator to have direct control over the entire network, and
make changes quickly and efficiently. Screening of all retinopathy diseases can be performed
effectively and can easily identify people who are at risk of vision loss. The reports of the user
can be available in less time e.g., 60 seconds. The system is fully automated, flexible to provide
image quality feedback effectively.
[0053] FIG. 2 illustrates a flow chart of the process for detecting retinopathy in an eye of a
subject, in accordance with an embodiment of the present disclosure.
[0054] Referring to FIG. 2, the method 200 for detecting retinopathy in an eye of a subject,
the method includes receiving 202, at a computing device from an image capturing device 102,
the one or more images of the eye of the subject. The received one or more images can be
analysed 204 to extract a set of attributes from the one or more images. The method includes
classifying 206, at the computing device, the extracted set of attributes based on matching of the
extracted set of attributes with a reference set of attributes, the classification pertaining to the
health attributes of the eye.
11
[0055] In an embodiment, the method further includes extracting 208, at the computing
device from the extracted set of attributes, a set of values for the extracted set of attributes,
wherein, based on a combination of classification of the extracted attributes and a deviation of
the extracted set of values for the extracted set of attributes from a reference set of values, the
processor can be configured to determine a diagnosis for the eye of the subject, the diagnosis
pertaining to state of retinopathy of the eye.
[0056] The computing device can include processor that can be in communication with
each of a memory, and input/output devices.The processor may include a microprocessor or
other devices capable of being programmed or configured to perform computations and
instruction processing in accordance with the disclosure. Such other devices may include
microcontrollers, digital signal processors (DSP), complex programmable logic device (CPLD),
field programmable gate arrays (FPGA), application-specific assimilated circuits (ASIC),
discrete gate logic, and/or other assimilated circuits, hardware or firmware in lieu of or in
addition to a microprocessor.
[0057] The memory includes programmable software instructions that are executed by the
processor. The processor may be embodied as a single processor or a number of processors. The
processor and a memory may each be, for example located entirely within a single computer or
other computing device. The memory, which enables storage of data and programs, may include
random-access memory (RAM), read-only memory (ROM), flash memory and any other form of
readable and writable storage medium.
[0058] FIG. 3 illustrates an exemplary computer system in which or with which
embodiments of the present invention can be utilized in accordance with embodiments of the
present disclosure.
[0059] As shown in FIG. 3, computer system 300 includes an external storage device 310,
a bus 320, a main memory 330, a read only memory 340, a mass storage device 350,
communication port 360, and a processor 370. A person skilled in the art will appreciate that
computer system may include more than one processor and communication ports. Examples of
processor 370 include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or
AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™
system on a chip processors or other future processors. Processor 370 may include various
modules associated with embodiments of the present invention. Communication port 360 can be
12
any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a
Gigabit or 10 Gigabit port using copper or fibre, a serial port, a parallel port, or other existing or
future ports. Communication port 360 may be chosen depending on a network, such a Local
Area Network (LAN), Wide Area Network (WAN), or any network to which computer system
connects.
[0060] Memory 330 can be Random Access Memory (RAM), or any other dynamic
storage device commonly known in the art. Read only memory 340 can be any static storage
device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing
static information e.g., start-up or BIOS instructions for processor 370. Mass storage 350 may be
any current or future mass storage solution, which can be used to store information and/or
instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced
Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk
drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or
Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda 7200 family)
or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of
Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from
various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and
Enhance Technology, Inc.
[0061] Bus 320 communicatively couples processor(s) 370 with the other memory,
storage, and communication blocks. Bus 320 can be, e.g. a Peripheral Component Interconnect
(PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like,
for connecting expansion cards, drives and other subsystems as well as other buses, such a front
side bus (FSB), which connects processor 370 to software system.
[0062] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a
cursor control device, may also be coupled to bus 320 to support direct operator interaction with
computer system. Other operator and administrative interfaces can be provided through network
connections connected through communication port 360. External storage device 310 can be any
kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only
Memory (CD-ROM), Compact Disc - Re-Writable (CD-RW), Digital Video Disk - Read Only
Memory (DVD-ROM). Components described above are meant only to exemplify various
13
possibilities. In no way should the aforementioned exemplary computer system limit the scope of
the present disclosure.
[0063] It should be apparent to those skilled in the art that many more modifications
besides those already described are possible without departing from the inventive concepts
herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the
appended claims. Moreover, in interpreting both the specification and the claims, all terms
should be interpreted in the broadest possible manner consistent with the context. In particular,
the terms “comprises” and “comprising” should be interpreted as referring to elements,
components, or steps in a non-exclusive manner, indicating that the referenced elements,
components, or steps may be present, or utilized, or combined with other elements, components,
or steps that are not expressly referenced. Where the specification claims refer to at least one of
something selected from the group consisting of A, B, C … and N, the text should be interpreted
as requiring only one element from the group, not A plus N, or B plus N, etc. The foregoing
description of the specific embodiments will so fully reveal the general nature of the
embodiments herein that others can, by applying current knowledge, readily modify and/or adapt
for various applications such specific embodiments without departing from the generic concept,
and, therefore, such adaptations and modifications should and are intended to be comprehended
within the meaning and range of equivalents of the disclosed embodiments. It is to be understood
that the phraseology or terminology employed herein is for the purpose of description and not of
limitation. Therefore, while the embodiments herein have been described in terms of preferred
embodiments, those skilled in the art will recognize that the embodiments herein can be
practiced with modification within the spirit and scope of the appended claims.
[0064] While various embodiments of the present disclosure have been illustrated and
described herein, it will be clear that the disclosure is not limited to these embodiments only.
Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to
those skilled in the art, without departing from the spirit and scope of the disclosure, as described
in the claims.
ADVANTAGES OF THE PRESENT DISCLOSURE
[0065] The present disclosure provides a system can perform screening of all retinopathy
diseases effectively and can easily identify people who are at risk of vision loss.
14
[0066] The present disclosure provides a secure and cost-effective system for detecting
retinopathy.
[0067] The present disclosure provides a system that can enable the use of secure SDN
technology.
[0068] The present disclosure provides a system in which the reports of the user can be
available in less time. The system is fully automated, flexible to provide image quality feedback
effectively.

We Claim:

1. A system (100) for detecting retinopathy in an eye of a subject, the system comprising:
an image capturing device (102) configured to obtain one or more images of the
eye of the subject; and
a processor (104) operatively coupled with a memory, said memory storing
instructions executable by the processor to:
receive, from the image capturing (102) device, the one or more images of
the eye of the subject;
analyse the received one or more images to extract a set of attributes from
the one or more images;
classify the extracted set of attributes based on matching of the extracted
set of attributes with a reference set of attributes, the classification pertaining to a
plurality of health attributes of the eye; and
extract, from the extracted set of attributes, a set of values for the extracted
set of attributes,
wherein, based on a combination of classification of the extracted attributes and a
deviation of the extracted set of values for the extracted set of attributes from a
reference set of values, the processor is configured to determine a diagnosis for
the eye of the subject, said diagnosis pertaining to state of retinopathy of the eye.
2. The system as claimed in claim 1, wherein the determined diagnosis is displayed on a
display device operatively coupled to the processor.
3. The system as claimed in claim 1, wherein the system comprises a memory device
operatively coupled with the processor (104), the memory device (110) configured to
store a log of operations of the system, the log of operations comprising any or a
combination of the one or more images, the extracted set of attributes, the reference set of
attributes, the classification of the extracted set of attributes, the extracted set of values
for the extracted attributes, the reference set of values for the extracted attributes and the
determined diagnosis for the eye of the user.
4. The system as claimed in claim 1, wherein the memory device (110) is a cloud storage.
16
5. The system as claimed in claim 1, wherein the processor is operatively coupled to a
learning engine 114, the learning engine trained to detect retinopathy of the eye of the
user.
6. The system as claimed in claim 5, wherein the learning engine is trained using a historical
data of correlation of the extracted set of attributes of a received one or more images of
the eye with a diagnosis for the eye, said diagnosis pertaining to a state of retinopathy of
the eye.
7. The system as claimed in claim 5, wherein the learning engine comprises any or a
combination of convolutional neural networks (CNN) and deep neural network (DNN).
8. The system as claimed in claim 1, wherein the controller of SDN configured to support
the underlying network and secure the data traversed by multiple features of SDN.
9. A method (200) for detecting retinopathy in an eye of a subject, the method comprising:
receiving (202), at a computing device from an image capturing device, the one or
more images of the eye of the subject;
analysing (204), at the computing device, the received one or more images to
extract a set of attributes from the one or more images;
classifying (206), at the computing device, the extracted set of attributes based on
matching of the extracted set of attributes with a reference set of attributes, the
classification pertaining to a plurality of health attributes of the eye; and
extracting (208), at the computing device from the extracted set of attributes, a set
of values for the extracted set of attributes,
wherein, based on a combination of classification of the extracted attributes and a
deviation of the extracted set of values for the extracted set of attributes from a reference
set of values, the processor is configured to determine a diagnosis for the eye of the
subject, said diagnosis pertaining to state of retinopathy of the eye.
10. A device for detecting retinopathy in an eye of a subject, the device comprising:
an image capturing device (102) configured to obtain one or more images of the
eye of the subject; and
a processor (104) operatively coupled with a memory, said memory storing
instructions executable by the processor to:
17
receive, from the image capturing device, the one or more images of the eye of
the subject;
analyse the received one or more images to extract a set of attributes from the one
or more images;
classify the extracted set of attributes based on matching of the extracted set of
attributes with a reference set of attributes, the classification pertaining to a plurality of
health attributes of the eye; and
extract, from the extracted set of attributes, a set of values for the extracted set of
attributes,
wherein, based on a combination of classification of the extracted attributes and a
deviation of the extracted set of values for the extracted set of attributes from a reference
set of values, the processor is configured to determine a diagnosis for the eye of the
subject, said diagnosis pertaining to state of retinopathy of the eye.