Cataract Detection using Deep Learning

Abstract

In a human life a very common disorder that occurs is, eye disorder named CATARACT, this eye disorder is

mostly common in humans within the age group of 40–50 years. If neglected can lead to eye blindness.

One can avoid this type of eye disorder from getting worse by detecting it on-time.

So we are proposing our model that uses Deep Learning to detect this disorder, using Fundus Images.

The model contains fully connected hidden layers with cost function and activation functions to properly

train the model and optimize it and to signi?cantly reduce the computational cost compared to other

models. Total 1130 affected and 1130 non affected images are feeded to model in order to train model

properly so that model doesn’t get overfitted.

RELATED WORKS

Modern automatic cataract detection methods comprise of three steps: feature extraction, pre-processing,

and classi?cation. Based on the algorithms employed in the feature extraction or classi?cation stages,

these techniques are divided into two groups: machine learning (ML)-based and deep learning (DL)-based

methods. These techniques have been covered in recent studies [9] -[12]. We quickly review a few of the

most important works from both groups in this section.

A. Past Works Based On Machine Learning

There are many [13] Proposed techniques for cataract identi?cation, designed for widespread screening

or as a step before classifying cataracts.The study focused on training the linear discriminant analysis

(LDA) algorithm.

using an improved texture feature.A clinical database experiment's results showed an accuracy of 84.8%.

A three-step automated cataract detection approach was proposed by Yang et al. A top to bottom hat

transition was used to increase the foreground/background contrast. Features were thought to be the

luminance and texture.

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Cataract Detection using Deep Learning

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Cataract Detection using Deep Learning

Abstract

RELATED WORKS

A. Past Works Based On Machine Learning

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