A Novel Approach for Detection of Hard Exudates Using Random Forest Classifier

Pratheeba, C.; Singh, N. Nirmal

doi:10.1007/s10916-019-1310-9

Cited by 18 publications

(11 citation statements)

References 24 publications

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“…The mathematical representation of accuracy, specificity, sensitivity, and F-score is denoted in the Eq. (11)(12)(13)(14).…”

Section: Resultsmentioning

confidence: 99%

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Enhanced Optimization using Advanced Cuckoo Search Algorithm with Ensemble classification for detection of Diabetic Retinopathy

Basavaraju¹,

Ganesarathinam²

2022

INDJCSE

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Diabetic Retinopathy (DR) is an eye disease which occurs due to enormous glucose level in the blood among diabetic patients. The diabetic patients are having higher chances of getting blindness if the sugar level is increased in body. An identification of landmark features present in the fundus images has to accurately find the features from the optic disc of fundus images. The existing researches used various Artificial Intelligence (AI) techniques for screening and diagnosing the DR earlier to prevent the diabetic patients from blindness which was determined based on the level of DR severity. However, the existing models were efficient that consumed time and the premature convergence was resulted with the drawback for the real world optimization approach. Thus, the proposed Dynamic weighted based Cuckoo Search Algorithm (DWCSA) is modified to improve the performances based on their basic structure. The higher convergence helped to train the model better for finding solutions. The proposed model overcomes the constraint issues occurred during feature selection process showed improvement in the accuracy. The proposed DWCSA obtained the accuracy of 97.46 % compared to the existing CNN model that obtained 98.94 % of accuracy for DIARETDB1 whereas, the eophtha dataset obtained accuracy of 98.91% for the proposed model.

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“…The mathematical representation of accuracy, specificity, sensitivity, and F-score is denoted in the Eq. (11)(12)(13)(14).…”

Section: Resultsmentioning

confidence: 99%

“…Pratheeba and Nirmal Singh [14] utilized Random Forest Classifier that was used for detecting the hard exudates. The novel classification technique was applied to improve the automatic detection of hard exudates from the colored retinal images.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Optimization using Advanced Cuckoo Search Algorithm with Ensemble classification for detection of Diabetic Retinopathy

Basavaraju¹,

Ganesarathinam²

2022

INDJCSE

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“…Altogether, more than 150 articles published since 2015 were collected from reputable journals across the World Wide Web, out of which 81 of those research articles [2015: (Ţălu et al 2015 ; Mahendran and Dhanasekaran 2015a , b ; Ibrahim et al 2015 ), 2016: (Banerjee and Kayal 2016 ; Partovi et al 2016 ; Wardoyo et al 2016 ; Abràmoff et al 2016 ; Prentašić and Lončarić 2016 ; Gulshan et al 2016 ; Akyol et al 2016 ; Gharaibeh 2016 ; Gharaibeh and Alshorman 2016 ), 2017: (Das and Puhan 2017 ; Dutta et al 2015 ; Tan et al 2017a , b ; Xu et al 2017 ; Quellec et al 2017 ; Raju et al 2017 ; Ting et al 2017 ; Al-Jarrah and Shatnawi 2017 ; Abbas et al 2017 ; Javidi et al 2017 ; Zhang et al 2017 ; Barkana et al 2017 ), 2018: (Kaur and Mittal 2018 ; Mansour 2018 ; Brown et al 2018 ; Randive et al 2018 ; Ramachandran et al 2018 ; Hemanth et al 2018 ; Saleh et al 2018 ; Seth and Agarwal 2018 ; Al-Hazaimeh et al 2018 ), 2019: (Gao et al 2018 ; Gonzalez-Gonzalo et al 2020 ; Sahlsten et al 2019 ; Liu et al 2019 ; Hemanth et al 2019 ; Sun 2019 ; Zhang et al 2019 ; Li et al 2019a , b ; Eftekhari et al 2019 ; Bellemo et al 2019 ; Pires et al 2019 ; Qummar et al 2019 ; Zeng et al 2019 ; Son et al 2019 ; Gulshan et al 2019 ; Nazir et al 2019 ; Nair and Muthuvel 2019 ; Chowdhury et al 2019 ; Pratheeba and Singh 2019 ; Memari et al 2019 …”

Section: Recent Development On the Automated Detection Of Dr Using Fu...mentioning

confidence: 99%

“…Articles with 1 entry each: (Banerjee and Kayal ( 2016 ) Partovi et al ( 2016 ) Prentašić and Lončarić ( 2016 ) Akyol et al ( 2016 ) Das and Puhan ( 2017 ) Kaur and Mittal ( 2018 ) Pratheeba and Singh ( 2019 ) Mateen et al ( 2020 )…”

Section: The Study Of the New Fundus Algorithms By Their Overall Mode...mentioning

confidence: 99%

Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

et al. 2022

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The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-022-10185-6.

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“…Early recognition of DR is the way to prevent the occurrence of blindness. The evolution of DR in the patient is determined by the presence of anomaly lesions, such as microaneurysms [2], hemorrhages [3], and hard exudates [4], [5]. This paper aims at providing an automated DR grading system that can not only recognize early DR occurrence but can also differentiate between the normal and the standard clinical stages of the disease, i.e., mild, moderate, severe, and PDR.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Grading System for Diabetic Retinopathy using Fundus Images. (Dept. E)

Elsawah¹,

Elnakib²,

Moustafa³

2020

MEJ. Mansoura Engineering Journal

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Diabetic Retinopathy (DR) is one of the main causes of blindness that can be overcome, if it is early detected. This work proposes an automated early detection and grading of DR using fundus images. The proposed detection and grading system investigate different deep learning architectures (i.e., ResNet and AlexNet) that are applied to an augment data to extract deep compact features of the fundus images. The extracted features are input to a pixel-wise Neural Network (NN) classifier or a Support Vector Machine (SVM) classifier for automated DR grading. The performance of the proposed system is evaluated using a publically available fundus Indian Diabetic Retinopathy Image Dataset (IDRiD), collected for ISBI-2018 challenge. The IDRiD dataset consists of 516 retinal images of normal and different DR grades, i.e., mild, moderate, severe, and Proliferative Diabetic Retinopathy (PDR). Our system achieves an overall accuracy of 95.73%, sensitivity of 95.73%, and specificity of 98.51% utilizing an AlexNet-based architecture and a pixel-wise NN classifier. Compared to the previous related work, the proposed system shows promising DR grading performance.

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A Novel Approach for Detection of Hard Exudates Using Random Forest Classifier

Cited by 18 publications

References 24 publications

Enhanced Optimization using Advanced Cuckoo Search Algorithm with Ensemble classification for detection of Diabetic Retinopathy

Enhanced Optimization using Advanced Cuckoo Search Algorithm with Ensemble classification for detection of Diabetic Retinopathy

Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

Deep Learning Grading System for Diabetic Retinopathy using Fundus Images. (Dept. E)

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