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Diagnosis of Eye Diseases Disease Prediction using Machine Learning 123
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(a) (b) (c) (d)
Fig. (9). Classification of images as (a) non-cataract, (b) mild cataract, (c) moderate cataract, and (d) severe
cataract.
Table 4 reviews the study in the literature about the findings and the limitation of
the different eye diseases in which deep learning technique is used.
Table 4. Different methods for detection of eye diseases
Algorithm Dataset Method Findings and Performance Short- comings Focus
The images can be
K.Shankar
et al. [45],
2020
Zhenhua
Wang
et al., [36],
2020
Messidor
Local OCT
dataset
having 100
images
Synergic
Deep
Learning
(SDL)
Kmeans
clustering and
build on
Selective
Binary and
Gaussian
Filtering
regularized
level set
(SBGFRLS)
SDL includes SDLk having
three parts input layer, k
DCNN component and C2 k
Synergic Network (SN)
which provides better
classification as compared to
other experimental models.
Accuracy 99.28%, specificity
99.38%. Sensitivity 98.54%.
The SBGFRLSOCT achieves
comparable accuracy,
specificity, and sensitivity as
manual DME segmentation,
but with a considerably
shorter processing time.
Sensitivity 91.8%, Precision-
97.7%, and Specificity-
99.2%
enhanced by using
various filtering
accuracy and
inclusion of some
other models like
AlexNet and
Inception results
in an increase of
the performance
of the model.
Dataset too small
DR
classifies
All four
severity
levels
Diabetic
Macular
Edema
124 Disease Prediction using Machine Learning Dubey and Dixit
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(Table 4) co nt.....
Algorithm Dataset Method Findings and Performance Short- comings Focus
Exudate segmentation and
class imbalance are
addressed. A mixture of
DCNN and a meta-heuristic
function selection strategy
replaces exudate
segmentation & unbalanced
data sets issues are solved.
Can be used for medical tasks
with ease. Accuracy =
97.26% (detection of fovea)
The system works in two
stages- the first is optic disc
region segmentation the pre-
trained DCNN for
classification and SVM for
learning features. It has been
found that ensemble methods
give better results. RIM-
ONE, ACRIMA,
DRISHTIGS1, and ORIGA
with the accuracy of 97.37%,
99.53%, 86.84%, and
90.00%, and AUC of 100%,
99.8%, 91.67%, and 92.06%,
respectively
On this application, the
proposed method performed
well without having any
highly advanced DL system
nor a database of millions of
images are needed. accuracy -
87%.
-
Model is complex
and hence cannot
be handle by
everyone.
DNN, only gives
the picture and
related name,
features are not
clearly specified
(e.g. SDD, IHRF,
or HRF). The
images used in
this study are
from a single
clinical site.
Clinically
Significant
Macular
Edema
Glaucoma
AMD
Renoh
Chalakkal
et al., [46],
2020
Syna Sreng
et al. [48],
2020
Sajib Sah et
al. [48],
2019
DRIVE,
DIARET
DB1,
DRIVE,
IDRid,
MESSID
OR and
UoA-DR
RIMONE,
ACRIMA,
DRISHTI-
153 patients
OCT images
DCNN –
Feature
Extraction &
KNN
classifier
Deep Lab
V3+, DCNN
GS1, and
ORIGA
DCNN
Diagnosis of Eye Diseases Disease Prediction using Machine Learning 125
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(Table 4) co nt.....
Algorithm Dataset Method Findings and Performance Short- comings Focus
It has the ability to be more
sensitive for the early
detection of glaucoma. Thus,
Guangzho u
An et al.
[49], 2019
Turimerla
Pratap,
Priyanka
Kokil [50],
2019
Arun
Govindaih
et al. [50],
2018
fundus and
3-D OCT
images
Fundus
images
collected
from
different
labeled and
openaccess
datasets
AREDS
(150000)
images
Transfer
learning based
CNN Random
forest
Compute raided Deep
Transfer
Method and
SVM
VGG16
the model is more precise in
glaucoma diagnosis, leading
to improved regular clinical
glaucoma treatment. AUC of
0.94, 0.942, 0.944. 0.949 for
color fundus images, RNFL
thickness maps, macular
GCC thickness maps, RNFL
deviation maps respectively
The network reduces the time
and effort needed to train a
CNN from scratch The
findings also show that when
the training set is limited,
transfer learning (or fine-
tuning) is successful. As a
result, transfer learning
improves the accuracy of
cataract detection using
fundus images. These
approaches are particularly
beneficial to people living in
rural areas. Accuracy
92.91%.
VGG16 neural network is
proposed in which two
experiments are done. In the
first experiment, based on
clinical significance binary
classification of images are
done then further images are
classified into four classes as
No, early, Intermediate, and
Advanced AMD. Accuracy –
92.5% in two class problem
83.2% in 4classification
Some more
images should be
collected and
preprocessed
images should be
used.
-
In this study,
information only
from fundus
images is taken
into consideration
which is not
enough for the
AMD detection.
Fundus images
must be pre-
processed to
increase accuracy
by removing lens
glare and no
uniform lighting,
as well as other
noisy data.
Open-angle
glaucoma
Cataract
AMD
126 Disease Prediction using Machine Learning Dubey and Dixit
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(Table 4) co nt.....
Algorithm Dataset Method Findings and Performance Short- comings Focus
Jen Hong
Tan et al.
[5], 2018
402 normal
and 708
AMD
images
deep CNN
model of
fourteen
layers
The model is fully
autonomous, cost effective,
and compact, allowing it to
be used everywhere.
Accuracy – 10-fold 95.45%
cross validation and 91.17%
blindfold validation.
Training and
testing images
should be
different to make
them more
efficient
AMD
RESEARCH CHALLENGES
The iterature study reveals that both deep learning (DL) and computer vision have
made significant contributions to the field of medical imaging and optical image
recognition for various eye disorders, such as DR, AMD, glaucoma, and retinal
detachment. The utilization of these techniques has enabled timely and automated
detection, addressing the issue of the limited doctor-patient ratio. Numerous deep
learning methods have been explored, but since deep learning has unique
specifications compared to traditional applications, further research is necessary
in this domain.
Despite the advantages offered by these techniques, researchers may encounter
certain challenges. Developing diagnostic tools requires access to annotated
datasets with a diverse collection of fundus images captured from various
locations using different fundus cameras. The abundance of fundus images with
varying color variations and patterns makes the comparison of computer vision
and fundus images more challenging.
Additionally, there is a shortage of specialists, and the number of people with eye
problems is increasing, leading to difficulties in providing timely and adequate
treatment, resulting in potential vision loss. Manual image analysis by doctors can
take several days, causing delays in patient care. Hence, the need for automation
in the field becomes evident, addressing the challenges and improving the
efficiency and accuracy of eye disorder detection.
CONCLUSION
This chapter provides a comprehensive review of eye disease detection techniques
utilizing computer vision and deep learning. It introduces the deep learning
framework, with a focus on the CNN approach and its significance in disease
diagnosis. Various imaging equipment, such as ultrasound imaging, fundus
photography, OCT, FA, and HDT, are discussed, along with their advantages and
limitations.
Diagnosis of Eye Diseases Disease Prediction using Machine Learning 127
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The chapter also delves into different eye diseases, including DR, Macular DR,
AMD, glaucoma, and cataract, exploring their symptoms, risk factors, and
existing research in the field. The research identifies a current research gap in
ophthalmic disorder detection through digital image processing, particularly
regarding the challenge of detecting multiple diseases simultaneously.
Considering the aging population and rising healthcare costs, advancements in
this field are crucial. Efforts to interpret the “black box” design of DL systems are
ongoing, but further research is needed in this area. Additionally, developing
better prediction algorithms to categorize patients into various risk groups and
treatment arms is essential to deliver personalized medicine to a global
population. Overall, this review highlights the importance of ongoing research
and innovation in eye disease detection and diagnosis.
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Disease Prediction using Machine Learning, 2024, 131-145 131
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CHAPTER 8
The Fusion of Human-Computer Interaction and
Artificial Intelligence Leads to the Emergence of
Brain Computer Interaction
M. Kiruthiga Devi
1
Department of Computer Science and Engineering, Sri Sai Ram Engineering College, Chennai,
India
Abstract: A personal computer may be used, with input devices such as a keyboard,
mouse, and joystick serving as an interface between the computers and the human. The
euphemistic, physically challenged are unable to use these computer systems, therefore,
BCI technology has advanced external applications to be managed without physical
movements in order to assist these physically disabled people and address the
limitations of HCI. The technological advancement in the field of cognitive
neuroscience and brain imaging has enabled it to communicate directly with the human
brain instead of using an interface. Rather than generating signals from muscle
movements, these systems use brain activity to monitor computers or communication
devices. Researchers in the field of Human-Computer Interaction (HCI) look at ways
for machines to utilize as many sensory sources as possible. Furthermore, researchers
have begun to consider implicit types of data, input that is not specifically performed to
instruct a machine to perform a task. Systems can evolve dynamically based on this
data in order to assist the user with the task at hand. Here we discussed components of
Brain-Computer Interface, its characteristics and challenges. The researchers are
attempting to replace conventional classifiers with Convolutional neural networks
(CNNs) that would provide a promising advantage in classification. The EEG signals
from the brain can be linked seamlessly to mechanical systems via BCI applications,
making it a rapidly growing technology that has applications in fields such as Artificial
Intelligence and Computational Intelligence.
1,*
Keywords: Actions, Atmosphere, Artificial intelligence, Brain computer
interface, Convolutional neural networks, Classifiers, Communication,
Computational, Deep Learning, Electroencephalographic, EEG, Human-computer
interaction, Input devices, Movements, Machines, Machine learning, Mechanical
system, Neuroscience, Physiology, Signals, Sensory, Support vector.
*
Engineering College, Chennai, India; E-mail: kiruthigaprofessor89@gmail.com
Corresponding author M. Kiruthiga Devi: Department of Computer Science and Engineering, Sri Sai Ram
Geeta Rani, Vijaypal Singh Dhaka & Pradeep Kumar Tiwari (Eds.)
All rights reserved-© 2024 Bentham Science Publishers
132 Disease Prediction using Machine Learning M. Kiruthiga Devi
https://t.me/med1917
INTRODUCTION
Sensors are used to track some of the physical processes in the brain associated
with specific types of thought [1]. As a result of these developments, braincomputer interfaces (BCIs) have been developed, allowing communication among
systems that do not depend on the brain's usual peripheral nerve and muscle
systems. Many factors have aided this advancement, including improved
understanding of neurobiological processes, machine learning algorithms, and
deep learning. A personal computer may be used as an interface between
computers and humans with input devices such as a keyboard, mouse, etc. [2].
The disabled persons are unable to use these systems, but in order to assist these
euphemistic physically challenged people and address the limitations of HCI, BCI
technology has advanced, allowing external applications to be managed without
the use of physical muscle movements. BCI calibration is difficult with two
stages: the low signal-to-noise ratio (SNR) and high subject-to-subject variability.
The amount of time needed for calibration varies depending on the type of
paradigm used. It can still be reduced by half. If brain signals have a low Signalto-Noise Ratio (SNR), BCI is most challenging when it comes to accurately
determine human intentions [3]. The reality is that BCI's real-world
implementation is limited by poor generalization potential and low classification
accuracy.
Deep learning approaches have been used to work with brain knowledge in recent
years to address the aforementioned challenges. While normally machine learning
algorithms need to select features manually, deep learning can learn complex
high-level features directly from brain signals, and its accuracy scales well with
the size of the training data set [4]. It is possible to monitor the activity of the
brain using different techniques, which can be categorized into two types:
invasive and non-invasive. The non-invasive BCI system majorly uses
Electroencephalogram (EEG) signals to capture brain signals from electrodes
mounted on the scalp. EEG data is extremely noisy, therefore, it can be difficult to
extract a coherent signal when your brains communicate through your scalp into
the EEG sensor. As a result, it's critical to extract useful information from
distorted brain signals and to develop a strong BCI system. When data from
electroencephalographic (EEG) sensors is processed through brain-computer
interfaces (BCIs), the number of channels, the amount of training data, and the
signal-to-noise ratio affect the accuracy of classification [5]. In real-world
applications, the SNR is the most difficult to modify of all these variables. The
development of several preprocessing and feature engineering methods for
reducing noise has been time-consuming and may result in information loss in the
extracted features. Feature engineering is heavily reliant on human domain
knowledge. Human experience can aid in capturing characteristics of some
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