Explainable AI based Glaucoma Detection using Transfer Learning and LIME Touhidul Islam Chayan

2025-04-27 0 0 1.9MB 6 页 10玖币

侵权投诉

Explainable AI based Glaucoma Detection using

Transfer Learning and LIME

Touhidul Islam Chayan

Computer Science and Engineering

BRAC University

Dhaka, Bangladesh

touhidul.islam.chayan@g.bracu.ac.bd

Anita Islam

Computer Science and Engineering

BRAC University

Dhaka, Bangladesh

anita.islam@g.bracu.ac.bd

Eftykhar Rahman

Computer Science and Engineering

BRAC University

Dhaka, Bangladesh

eftykhar.rahman@g.bracu.ac.bd

Md. Tanzim Reza

Computer Science and Engineering

BRAC University

Dhaka, Bangladesh

tanzim.reza@bracu.ac.bd

Tasnim Sakib Apon

Computer Science and Engineering

BRAC University

Dhaka, Bangladesh

sakibapon7@gmail.com

MD. Golam Rabiul Alam

Computer Science and Engineering

BRAC University

Dhaka, Bangladesh

rabiul.alam@bracu.ac.bd

Abstract—Glaucoma is the second driving reason for partial

or complete blindness among all the visual deﬁciencies which

mainly occurs because of excessive pressure in the eye due to

anxiety or depression which damages the optic nerve and creates

complications in vision. Traditional glaucoma screening is a time-

consuming process that necessitates the medical professionals’

constant attention, and even so time to time due to the time

constrains and pressure they fail to classify correctly that

leads to wrong treatment. Numerous efforts have been made to

automate the entire glaucoma classiﬁcation procedure however,

these existing models in general have a black box characteristics

that prevents users from understanding the key reasons behind

the prediction and thus medical practitioners generally can not

rely on these system. In this article after comparing with various

pre-trained models, we propose a transfer learning model that

is able to classify Glaucoma with 94.71% accuracy. In addition,

we have utilized Local Interpretable Model-Agnostic Explana-

tions(LIME) that introduces explainability in our system. This

improvement enables medical professionals obtain important and

comprehensive information that aid them in making judgments.

It also lessen the opacity and fragility of the traditional deep

learning models.

Index Terms—Biomedical Image Processing, Glaucoma, Blind-

ness, Machine Learning, Convolutional Neural Network, Explain-

able AI.

I. INTRODUCTION

Glaucoma is a very common group of eye diseases caused

by damage to the optic nerve that connects the eye to the brain

and if untreated, it causes permanent loss of vision which is

the second most popular cause of blindness globally. It is also

known as the ‘silent thief of sight’ as it cannot be detected at

a very early stage [1]. Around 57.5 million people worldwide

are affected by Glaucoma [2]. There are two signiﬁcant kinds

of glaucoma: open-angle and angle-closure. Movement of

glaucoma can be halted with medicines, however, part of the

vision that is now lost can’t be reestablished. This is the reason

it’s vital to distinguish early indications of glaucoma with

standard eye tests. Acute angle-closure glaucoma is a visual

crisis and requires quick consideration through early detection.

Glaucoma can be diagnosed and partial or complete blindness

could be prevented if we can detect it in an early stage.

Unfortunately, not many people bother about the early

detection of Glaucoma whereas it can be diagnosed early to

prevent eyesight loss. For this reason, we have decided to work

with the early detection of glaucoma disease and going to

use Explainable AI (XAI) to classify scanned images of eyes

that have glaucoma that proposes the report to the decision of

Artiﬁcial Intelligence which means Deep Learning or Black

Box to the extent that is human interpretable. Moreover, we

intend to give an outline of ongoing distributions in regards

to the utilization of man-made consciousness to improve the

recognition and treatment of glaucoma. Deep Learning (DL) is

a subset of Artiﬁcial Intelligence (AI) dependent on profound

neural networks which have made striking leaps forwards

in clinical imaging, especially for image characterization

and pattern acknowledgement [3]. The main purpose of this

study is to represent whether and how deep learning based

measurements can be utilized for glaucoma execution in the

clinic [4]. On the other hand, if the vision loss has already

occurred, the treatment can delay or hinder further vision

loss [5]. Open-angle glaucoma is the most common form

of glaucoma and is responsible for 90% of the cases [6].

Fundus pictures can be utilized for glaucoma ﬁnding through

the CDR strategy [7]. Such CNN models can work in pairs

with human specialists to keep up with large eye health and

assist recognition of visual deﬁciency causing eye sickness

[8]. Our objective are as follows: (i) Automating the process

for Glaucoma categorization. (ii) Provide detailed information

to medical professionals against a prediction, so that they can

rely on the system. (iii) Increase the efﬁciency of the entire

process. (iv) Reduce the amount of work required by the

medical personnel. Additionally, we encountered a number of

challenges or obstacles while performing our study, including

dealing with the tendency of models to overﬁt data, computing

resources, etc.

arXiv:2210.03332v1 [cs.CV] 7 Oct 2022

TABLE I

COMPARISON BETWEEN THE PREVIOUS STUDIES

Architectures Dataset Accuracy Reference

CNN Retinal OCT 94.87% [19]

ResNet50-v1 Retinal OCT 94.92 % [20]

CNN Glaucoma 84.50% [10]

FNN Glaucoma 92.5% [11]

The signiﬁcant contributions of this article are stated as

follows:

•A transfer learning based Glaucoma disease detection

model is proposed and a comprehensive study between

various pre-trained model’s performance on Glaucoma is

conducted.

•Evaluating the interpretability of the proposed model

using Local Interpretable Model-Agnostic Explana-

tions(LIME) that offeres the medical practitioners with

key features or information for the accurate classiﬁcation

of visual diseased Glaucoma.

•Performance of the proposed model has been studied on

a benchmark Glaucoma dataset.

A brief overview of previous Glaucoma disease classiﬁca-

tion research is included in Section II of this paper, followed

by a brief discussion of our methodology, models, and tech-

niques in Section III, which is divided into ﬁve sub-sections.

The explanation of the work plan is provided in section III-

A, whereas III-B discusses data set description, and Section

III-C exposes our proposed CNN model. The performance

evaluation have been depicted in Section IV. Finally, in Section

V we have interpreted our model and attempted to illustrate

how it makes decisions.

II. LITERATURE REVIEW

Glaucoma is one of the most common causes of permanent

blindness around the world [9]. As when the pressure inside

the eye is too high in a particular nerve that moment glaucoma

will develop and it will also create eye ache. The working

mechanisms of the different diagnosis tools like tonometers,

gonioscopy, scanning laser tomography, etc are available for

the treatment and detection but there are some advantages and

disadvantages which sometimes create boundaries. For this,

there should be an evaluation of how this works. But with

using deep learning the boundaries can be removed. As the

XAI concept can be understood by humans which will be

closer to the human brain to understand. We have utilized

ImageNet’s various pre-trained models in order to classify

diseased Glaucoma.

Table I depicts the brief illustration of previous studies.

Additionally, one more research was done from which We

learned The impact of artiﬁcial intelligence in the diagnosis

and management of glaucoma from [12]. Computerized auto-

mated visual ﬁeld testing represents a signiﬁcant improvement

in mapping the island of vision, allowing visual ﬁeld testing to

become a cornerstone in diagnosing and managing glaucoma.

Goldbaum developed a two-layer neural network for analyzing

visual ﬁelds in 1994 et al. [7]. This network classiﬁed normal

and glaucomatous eyes with the same sensitivity (65%) and

speciﬁcity (72%) as two glaucoma specialists.

The pathogenesis of glaucoma appears to be dependent

on several interconnected pathogenetic mechanisms, including

mechanical effects characterized by excessive intraocular pres-

sure, reduced neutrophil produce, hypoxia, excitotoxicity, ox-

idative stress, and the involvement of autoimmune processes,

according to new evidence [13]. Hearing loss has also been

linked to the development of glaucoma. In normal tension

glaucoma patients with hearing loss, antiphosphatidylserine

antibodies of the immunoglobulin G class were shown to

be more prevalent than in normal-tension glaucoma patients

with normacusis. The World Health Organization reports that

glaucoma affects approximately 60 million people worldwide.

By the year 2020, it is expected that approximately 80 million

people will suffer from glaucoma, which is anticipated to result

in 11.2 million cases of bilateral blindness [14]. This is why

it needs to be treated as early as possible according to the

authors.

Unlike the studies mentioned above, our focus has been on

interpreting our proposed model such that medical practition-

ers would feel conﬁdent utilizing our approach.

III. METHODOLOGY

We can obtain a clear overview of our proposed model

which is separated into three subsections, from Section III.

Part III-A discusses about our working plan, followed by part

III-B, which discusses data gathering and pre-processing, and

lastly, part III-C, which discusses the architecture.

A. System Model

We have employed Deep Learning or FCNNs in our work

which is a BlackBox function. Generally, Black boxes work

excellently but their structure won’t give you any insights that

will explain how the function is being approximated. For this,

we have used LIME which is one of the most popular XAI-

based python libraries. There are a lot of XAI frameworks

that explain the BlackBox model’s insights by features. XAI

functions work well in terms of explaining complex classiﬁca-

tion models. In short, these functions generate an explanation

through charts of graphs for a complex model’s prediction

which are also pretty fast. Figure 2 represents how black boxes

actually work with the help of LIME.

Here we can see BlackBox models generate a result or

output based on some features from the given/training datasets.

And through lime, we can have a visualization from which

features the output was based on. In our Glaucoma dataset,

we have some features for Suspicious glaucoma and Non-

glaucoma. In both sections, we have fundus images, and

labels as 1 as the conﬁrmed glaucoma case and 0 as the

Non-glaucoma case. To apply XAI, we took Fully Connected

Neural Networks (FCNNs) as a black box AI model to predict

glaucoma with the help of the data. To compile all of these

classiﬁcations and determine the average of these scores to one

single output, we will use ReLU non-linear activation function

文档加载中……请稍候！
如果长时间未打开，您也可以点击刷新试试。

下载文档到电脑，查找使用更方便

10 玖币 0人已下载

立即下载

摘要：

ExplainableAIbasedGlaucomaDetectionusingTransferLearningandLIMETouhidulIslamChayanComputerScienceandEngineeringBRACUniversityDhaka,Bangladeshtouhidul.islam.chayan@g.bracu.ac.bdAnitaIslamComputerScienceandEngineeringBRACUniversityDhaka,Bangladeshanita.islam@g.bracu.ac.bdEftykharRahmanComputerSciencea...

展开>> 收起<<

Explainable AI based Glaucoma Detection using Transfer Learning and LIME Touhidul Islam Chayan.pdf

共6页,预览2页

还剩页未读，继续阅读

声明：本站为文档C2C交易模式，即用户上传的文档直接被用户下载，本站只是中间服务平台，本站所有文档下载所得的收益归上传人(含作者)所有。玖贝云文库仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对上载内容本身不做任何修改或编辑。若文档所含内容侵犯了您的版权或隐私，请立即通知玖贝云文库，我们立即给予删除！

Explainable AI based Glaucoma Detection using Transfer Learning and LIME Touhidul Islam Chayan

相关推荐

开通VIP享超值会员特权

作者详情

相关内容

热门标签

举报选择: