Non-Proliferative Diabetic Retinopathy Classification Based on Hard Exudates Using Combination of FRCNN, Morphology, and ANFIS

Putra, Ricky Eka; Tjandrasa, Handayani; Suciati, Nanik; Wicaksono, Ardian Yusuf

doi:10.1109/icvee50212.2020.9243185

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…The proposed a suitable cluster centroid for grouping the bright yellowish pixels as a cluster by using FCM and the objective function Jm of the FCM is expressed as per Eq. (10).…”

Section: Pixel Grouping By Fcm Methodsmentioning

confidence: 99%

“…Firstly, the retinal images were classified by improved FCM method and then the EXs related features were grouped by RBF classifier, as a result EXs are segmented from fundus photography. With the flourish of clustering method, some automated EXs detection methods from fundus photography are proposed by R. E. Putra et al [10]. In this research, a faster region-based convolutional neural network and mathematical morphology method are applied to remove the OD region and then EXs features were segmented by using adaptive neuro-fuzzy inference system method.…”

Section: Previous Related Workmentioning

confidence: 99%

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Exudates Detection Using Fusion of Histogram-Based Fuzzy C-Means Clustering Based on a New Weight Assignment Scheme

Wisaeng

2023

Preprint

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Diabetic Retinopathy (DR) is one of the earliest signs of a complication of diabetes mellitus and is also a leading cause of visual blindness globally. Recent studies have proposed that exudates (EXs) are the hallmark of DR severity. The present study aims to accurately and automatically detect EXs related features that are difficult to detect in fundus photography in early stages. The proposed method utilizes a Fusion of Histogram-Based Fuzzy C-Means clustering (FHBFCM) based on a New Weight Assignment Scheme (NWAS), and a set of four selected features from stages of pre-processing in order to evolve the detection method. Through the coarse segmentation stage, the optimal parameter of FHBFCM is trained by the features of DR for detection of EXs diseases through a stepwise enhancement process. The histogram-based is used to find the color intensity in each of the pixels and performed to accomplish RGB color information. This RGB color information is used as the initial cluster centers for creating the appropriate region and generating the homogeneous regions by FCM method. Afterwards, the best expression of NWAS is then used for fine segmentation stage. The experimental results show that the proposed method achieves accuracy values of 96.12%, 97.20%, 93.22% for EXs segmentation on DiaretDB0, DiaretDB1, and STARE fundus image datasets respectively. In conclusion, this study provides a new method for early detection of EXs related features with a competitive accuracy and its ability to outperforms the other stateof-the-art by optimizing the detection quality, and possibly reducing the false positive segmentation in a significant way.

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“…The proposed a suitable cluster centroid for grouping the bright yellowish pixels as a cluster by using FCM and the objective function Jm of the FCM is expressed as per Eq. (10).…”

Section: Pixel Grouping By Fcm Methodsmentioning

confidence: 99%

Section: Previous Related Workmentioning

confidence: 99%

Exudates Detection Using Fusion of Histogram-Based Fuzzy C-Means Clustering Based on a New Weight Assignment Scheme

Wisaeng

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Firstly, the retinal images were classified by improved FCM and then the EXs were grouped by the RBF; as a result, EXs were segmented from retinal images. With the flourishing of the clustering method, some automated EXs detection is proposed by Putra et al [10]. This research applies a convolutional neural network and mathematical morphology to locate the OD region.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed suitable cluster centroid for grouping the bright yellowish pixels as a cluster by using FCM and the objective function J m of the FCM is expressed as per Eq. (10).…”

Section: Pixel Grouping By Fcmmentioning

confidence: 99%

A Novel Soft Clustering Method for Detection of Exudates

Wisaeng¹

2023

Computer Systems Science and Engineering

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One of the earliest indications of diabetes consequence is Diabetic Retinopathy (DR), the main contributor to blindness worldwide. Recent studies have proposed that Exudates (EXs) are the hallmark of DR severity. The present study aims to accurately and automatically detect EXs that are difficult to detect in retinal images in the early stages. An improved Fusion of Histogram-Based Fuzzy C-Means Clustering (FHBFCM) by a New Weight Assignment Scheme (NWAS) and a set of four selected features from stages of pre-processing to evolve the detection method is proposed. The features of DR train the optimal parameter of FHBFCM for detecting EXs diseases through a stepwise enhancement method through the coarse segmentation stage. The histogram-based is applied to find the color intensity in each pixel and performed to accomplish Red, Green, and Blue (RGB) color information. This RGB color information is used as the initial cluster centers for creating the appropriate region and generating the homogeneous regions by Fuzzy C-Means (FCM). Afterward, the best expression of NWAS is used for the delicate detection stage. According to the experiment results, the proposed method successfully detects EXs on the retinal image datasets of Dia-retDB0 (Standard Diabetic Retinopathy Database Calibration level 0), DiaretDB1 (Standard Diabetic Retinopathy Database Calibration level 1), and STARE (Structured Analysis of the Retina) with accuracy values of 96.12%, 97.20%, and 93.22%, respectively. As a result, this study proposes a new approach for the early detection of EXs with competitive accuracy and the ability to outperform existing methods by improving the detection quality and perhaps significantly reducing the segmentation of false positives.

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“…Penelitian ini melakukan pengujian untuk mengetahui 4 kernel SVM (linear, polynomial, gaussian dan sigmoid) yang memberikan akurasi, presisi, recall dan f1-score terbaik dalam mengklasifikasikan lantai dan tangga turun. Dalam mencari nilai akurasi rumus 6, mencari nilai presisi rumus 7, mencari nilai rumus 8, dan mencari nilai f1-score menggunakan rumus 9 [16].…”

Section: Gambar 5 Fungsi Kernel Merubah Sebaran Data Non-linear Menja...unclassified

Kernel Comparison on Support Vector Machine for Detecting Stairs Descent

Johan¹,

Wicaksono²,

Fauzi³

et al. 2022

Com, Engine, Sys, Sci

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Terdapat 4 kernel yang dapat digunakan dalam klasifikasi Support Vector Machine dalam membuat hyperplane. Keempat kernel tersebut adalah linear, polynomial, gaussian dan sigmoid. Setiap kernel dapat menghasilkan akurasi yang berbeda-beda. Hal ini dikarenakan pengaruh sebaran data yang diklasifikasikan. Terdapat 2 kelas yang diklasifikasikan, yaitu lantai dan tangga turun. Dilakukan proses ekstraksi fitur tekstur terhadap citra lantai dan tangga turun menggunakan metode Gray Level Co-occurence Matrix. Terdapat 7 fitur dari GLCM yang dihasilkan pada proses ekstraksi fitur. Selanjutnya dilakukan klasifikasi menggunakan Support Vector Machine dengan mencoba setiap kernelnya. Dari hasil pengujian didapatkan kernel linear menghasilkan akurasi yang paling tinggi, yaitu 89%. Kernel sigmoid mendapatkan akurasi 84%. Kernel Gaussian mendapatkan akurasi sebesar 85%. Sedangkan kernel polynomial mendapatkan akurasi yang paling rendah yaitu 78%.

show abstract

Non-Proliferative Diabetic Retinopathy Classification Based on Hard Exudates Using Combination of FRCNN, Morphology, and ANFIS

Cited by 11 publications

References 16 publications

Exudates Detection Using Fusion of Histogram-Based Fuzzy C-Means Clustering Based on a New Weight Assignment Scheme

Exudates Detection Using Fusion of Histogram-Based Fuzzy C-Means Clustering Based on a New Weight Assignment Scheme

A Novel Soft Clustering Method for Detection of Exudates

Kernel Comparison on Support Vector Machine for Detecting Stairs Descent

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