Peripapillary Atrophy Detection in Fundus Images Based on Sectors with Scan Lines Approach

Septiarini, Anindita; Pulungan, Reza; Harjoko, Agus; Ekantini, Retno

doi:10.1109/iac.2018.8780490

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…KNN is a supervised machine learning algorithm that can address classification and regression issues [24]. The vast majority of neighbors are considered input data.…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

“…The classification system is needed to immediately find out the symptoms suffered by the patient without convening the expert or doctor. It can be applied using several methods, such as rule-based [21]- [24], or using machine learning [25]. The following methods were used in prior studies to implement the machine learning-based classification process: naive Bayes [6], logistic regression [12], random forest (RF) [8], [12], [16], k-nearest neighbor (KNN) [26], artificial neural network (ANN) [10], [13], dan support vector machine (SVM) [8], [14].…”

Section: Introductionmentioning

confidence: 99%

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Dengue classification method using support vector machines and cross-validation techniques

Hamdani

Hatta

Puspitasari

et al. 2022

IJ-AI

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Dengue is a dangerous disease that can lead to death if the diagnosis and treatment are inappropriate. The common symptoms that occur, including headache, muscle aches, fever, and rash. Dengue is a disease that causes endemics in several countries in South Asia and Southeast Asia. There are three varieties of dengue, such as dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS). This disease can currently be classified using a machine learning approach with the input data being the dengue symptoms. This study aims to classify dengue types consisting of three classes: DF, DHF, and DSS using five classification methods including C.45, decision tree (DT), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). The dataset used consists of 21 attributes, which are the dengue symptoms. It was collected from 110 patients. The evaluation method was conducted using cross-validation with k-folds of 3, 5, and 10. The dengue classification method was evaluated using three parameters: precision, recall, and accuracy, which were most optimally achieved. The most optimal evaluation results were obtained using SVM with k-fold 3 and 10 with precision, recall, and accuracy values reaching 99.1%, 99.1%, and 99.1%, respectively.

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“…KNN is a supervised machine learning algorithm that can address classification and regression issues [24]. The vast majority of neighbors are considered input data.…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dengue classification method using support vector machines and cross-validation techniques

Hamdani

Hatta

Puspitasari

et al. 2022

IJ-AI

Self Cite

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“…https://doi.org/10.4258/hir.2023.29.2.145 these results may be subjective because they are influenced by differences in educational background, experiences, and psychological factors, especially when dealing with a large number of fundus images. The glaucoma features that need to be evaluated include the cup-to-disc ratio (CDR) [11,12], the neuroretinal rim (consisting of four sections: inferior, superior, nasal, and temporal), peripapillary atrophy (PPA) [13,14], and the retinal nerve fiber layer (RNFL) [15,16]. In previous studies, those features have been extracted and detected automatically.…”

mentioning

confidence: 99%

“…Deep learning methods have recently been developed with various models [12,[20][21][22][23]. Prior to implementing the segmentation method, pre-processing is applied for several purposes, such as forming a sub-image that focuses on capturing the optic disc area [12,13,17,23,24] to reduce the computation time in subsequent processes and to remove the blood vessels [12,[17][18][19][20] in order to overcome the influence of their presence. Image enhancement has also been applied to clarify the edges of the disc using contrast-limited adaptive histogram equalization (CLAHE) [12,25], filtering [12,17,21,25], and color space adjustment [3,17,24].…”

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confidence: 99%

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images

et al. 2023

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Objectives: The optic disc is part of the retinal fundus image structure, which influences the extraction of glaucoma features. This study proposes a method that automatically segments the optic disc area in retinal fundus images using deep learning based on a convolutional neural network (CNN).Methods: This study used private and public datasets containing retinal fundus images. The private dataset consisted of 350 images, while the public dataset was the Retinal Fundus Glaucoma Challenge (REFUGE). The proposed method was based on a CNN with a single-shot multibox detector (MobileNetV2) to form images of the region-of-interest (ROI) using the original image resized into 640 × 640 input data. A pre-processing sequence was then implemented, including augmentation, resizing, and normalization. Furthermore, a U-Net model was applied for optic disc segmentation with 128 × 128 input data.Results: The proposed method was appropriately applied to the datasets used, as shown by the values of the F1-score, dice score, and intersection over union of 0.9880, 0.9852, and 0.9763 for the private dataset, respectively, and 0.9854, 0.9838 and 0.9712 for the REFUGE dataset.Conclusions: The optic disc area produced by the proposed method was similar to that identified by an ophthalmologist. Therefore, this method can be considered for implementing automatic segmentation of the optic disc area.

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A Review on Glaucoma Disease Detection Using Computerized Techniques

et al. 2021

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Peripapillary Atrophy Detection in Fundus Images Based on Sectors with Scan Lines Approach

Cited by 8 publications

References 25 publications

Dengue classification method using support vector machines and cross-validation techniques

Dengue classification method using support vector machines and cross-validation techniques

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images

A Review on Glaucoma Disease Detection Using Computerized Techniques

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