Retinal Image Laser Marks Detection using a Convolutional Neural Network

Elrajubi, Osama Mohamed; Abuzaraida, Mustafa Ali; Zeki, Akram M.

doi:10.1109/3ict.2018.8855784

Cited by 2 publications

(6 citation statements)

References 7 publications

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“…The results were promising of almost accuracy rate average up to 89.60 %. comparing the results with the previous study [16], the average of the accuracy was slightly dropped. The previous study came out with an accuracy average of 91.65 % with just eight layers only.…”

Section: Discussionsupporting

confidence: 53%

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Laser Marks Classification for Retinal Images based on Convolutional Neural Network

Abuzaraida¹,

Elrajubi²

2019

IJEAT

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Recently, deep learning approaches have been getting more attention in many research fields. Medical imaging field has been attracting widely by deep learning techniques. An example of this field categories are images segmentations, images registration, images classification and retrieval of images database. This paper is presenting a number of experiments to classify rental images using Convolutional Neural Networks (CNN). These images of retinal could contain laser marks which left by the action of the laser on the surface of the retina. (CNN) is defined as trainable multi-stages architecture composed of multiple stages. The inputs and outputs of each stage are a set of arrays which called the feature maps. For the outputs, every feature map is representing a unique feature which extracted from all the regions which located on the input. Basically, every stage is consisted of three layers which are: a filter bank, a non linearity, and a layer of feature pooling. However, the classic (CNN) is normally consisting of three or less number of layers. The results accuracy were appropriate of more than 90%. As a summary of this paper, a number of considerations are listed for possible improvements and future developments.

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Section: Discussionsupporting

confidence: 53%

“…Several experiments were conducted in this study. However, these experiments are conducting to improve the results of the previous study [16]. In this study, 13 layers were used instead of 8 layers of the previous study.…”

Section: Details Of the Experiments And Resultsmentioning

confidence: 99%

Laser Marks Classification for Retinal Images based on Convolutional Neural Network

Abuzaraida¹,

Elrajubi²

2019

IJEAT

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“…The experiment results were auspicious with an average accuracy rates of 95.75% and reached to 97.95% as a top rate. This rate average is higher than the previous studies [16,17] with an average accuracy rate of 91.65% and 89.60% as shown in Table 2. However, both studies used a CNN network with 8 and 13 layers respectively and were tested on the same dataset that was used in this study for training and testing.…”

Section: Discussioncontrasting

confidence: 60%

“… Layer 1: As the same used dataset of previous studies [16][17], the images were sized at 227×227 pixels with level of RGB. Here, the normalization was 0 center for layer 1.…”

Section: Datasetmentioning

confidence: 99%

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Improving Laser Mark Detection for Retinal Images based on the AlexNet Model

Abuzaraida¹

2020

IJATCSE

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Recently, the field of deep learning has received increased attention due to its high accuracy. A common deep learning technique is Convolutional Neural Networks (CNN), which is as a construction of trainable multi-stages using multiple phases. In this paper, we use a type of CNN called AlexNet to classify human retinal images into 'normal' or 'have been treated using photocoagulation laser treatments' classes. Indeed, this classification technique will help experts to examine any case and make the examination process faster and more efficient. The study was conducted through several experiments using 730 images of human retina that were either treated by laser or not treated. An average accuracy rate of more than 97% was obtained. Additionally, possible improvements and destiny traits are suggested to summarize this study.

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Retinal Image Laser Marks Detection using a Convolutional Neural Network

Cited by 2 publications

References 7 publications

Laser Marks Classification for Retinal Images based on Convolutional Neural Network

Laser Marks Classification for Retinal Images based on Convolutional Neural Network

Improving Laser Mark Detection for Retinal Images based on the AlexNet Model

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