A Random Forest classifier-based approach in the detection of abnormalities in the retina

Chowdhury, Anirban; Chatterjee, Tamojit; Banerjee, Sreeparna

doi:10.1007/s11517-018-1878-0

Cited by 73 publications

(35 citation statements)

References 28 publications

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“…e point in a cluster is treated as the centroid if the sum of all the distances between this point and all the objects of the cluster achieve the optimal minimization. e main objective of NC is to minimize the sum of distances between the objects of a cluster and its centroid [47].…”

Section: Classification Methodsmentioning

confidence: 99%

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

García-Domínguez

Galván-Tejada

Zanella-Calzada

et al. 2020

Mobile Information Systems

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In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.

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Section: Classification Methodsmentioning

confidence: 99%

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

García-Domínguez

Galván-Tejada

Zanella-Calzada

et al. 2020

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…There are a lot of classifiers used to classify the abnormalities in the retina. Chowdhury et al [82] introduced a novel technique using random forest classifier (RFC) to help the ophthalmologists to accurately detect the abnormalities in the retinal images. The author applied a combination of K means clustering technique and machine learning methods, to classify the retinal images.…”

Section: ) Micro-aneurysms Detection Techniquesmentioning

confidence: 99%

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

et al. 2020

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Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a timeconsuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community. INDEX TERMS Artificial intelligence, deep learning, diabetic retinopathy, fundus images, machine learning, ophthalmology.

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“…In the testing phase, each tree classifies the testing instance and a majority voting technique is used to classify the instance. Random forest has been used in various domains such as astronomy [64] and medicine [65][66][67][68]. In this work, 100 decision tree classifiers are employed.…”

Section: The Random Forestmentioning

confidence: 99%

ECG-Based Subject Identification Using Statistical Features and Random Forest

et al. 2019

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In this work, a nonfiducial electrocardiogram (ECG) identification algorithm based on statistical features and random forest classifier is presented. Two feature extraction approaches are investigated: direct and band-based approaches. In the former, eleven simple statistical features are directly extracted from a single-lead ECG signal segment. In the latter, the single-lead ECG signal is first decomposed into bands, and the statistical features are extracted from each segment of a given band and concatenated to form the feature vector. Nonoverlapping segments of different lengths (i.e., 1, 3, 5, 7, 10, or 15 sec) are examined. The extracted feature vectors are applied to a random forest classifier, for the purpose of identification. This study considers 290 reference subjects from the ECG database of the Physikalisch-Technische Bundesanstalt (PTB). The proposed identification algorithm achieved an accuracy rate of 99.61% utilizing the single limb lead (I) with the band-based approach. A single chest lead (V1), augmented limb lead (aVF), and Frank’s lead (Vx) achieved an accuracy rate of 99.37%, 99.76%, and 99.76%, respectively, using the same approach.

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A Random Forest classifier-based approach in the detection of abnormalities in the retina

Cited by 73 publications

References 28 publications

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

ECG-Based Subject Identification Using Statistical Features and Random Forest

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