An Ensemble based Machine Learning model for Diabetic Retinopathy Classification

Gadekallu, Thippa Reddy; Bhattacharya, Sweta; Ramakrishnan, S.; Chowdhary, Chiranji Lal; Hakak, Saqib; Kaluri, Rajesh; Maddikunta, Praveen Kumar Reddy

doi:10.1109/ic-etite47903.2020.235

Cited by 113 publications

(55 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to strengthen the possibilistic c-mean algorithm, we have hybridization with an intuitionist fuzzy c-mean algorithm. The Intuitionist fuzzy c-mean algorithm used to solve uncertainty issues by addressing degree of hesitation during the membership function [ 51 , 52 , 53 ].…”

Section: Methodsmentioning

confidence: 99%

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Chowdhary

Mittal

Kumaresan

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

show abstract

Section: Methodsmentioning

confidence: 99%

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Chowdhary

Mittal

Kumaresan

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…An ensemble bagging classifier was used in the binary classification task. Reddy et al [18] applied an ensemble-based machine learning model including logistic regression, decision tree, random forest, adaboost, and k-nearest neighbor Classifiers on the DR dataset from the UCI machine learning repository. The model was trained on normalized datasets and the proposed ensemble-based model performed better than the individual machine learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification

Gürcan¹,

Beyca²,

Doğan³

2021

IJCIS

View full text Add to dashboard Cite

Diabetes is one of the emerging threats to public health all over the world. According to projections by the World Health Organization, diabetes will be the seventh foremost cause of death in 2030 (WHO, Diabetes, 2020. https://www.afro.who.int/healthtopics/diabetes). Diabetic retinopathy (DR) results from long-lasting diabetes and is the fifth leading cause of visual impairment, worldwide. Early diagnosis and treatment processes are critical to overcoming this disease. The diagnostic procedure is challenging, especially in low-resource settings, or time-consuming, depending on the ophthalmologist's experience. Recently, automated systems now address DR classification tasks. This study proposes an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep convolutional neural network (CNN) and machine learning methods. Features are extracted from a pretrained model by the transfer learning approach. DR images are classified by several machine learning methods. XGBoost outperforms other methods. Dimensionality reduction algorithms are applied to obtain a lowerdimensional representation of extracted features. The proposed model is trained and evaluated on a publicly available dataset. Grid search and calibration are used in the analysis. This study provides researchers with performance comparisons of different machine learning methods. The proposed model offers a robust solution for detecting DR with a small number of images. We used a transfer learning approach, which differs from other studies in the literature, during the feature extraction. It provides a data-driven, cost-effective solution, which includes comprehensive preprocessing and fine-tuning processes.

show abstract

“…Pada penelitian ini dibuat model machine learning untuk melakukan klasifikasi terhadap penyakit diabetik retinopati. Penelitian terbaru menunjukkan bahwa dari beberapa algoritma pembelajaran mesin, pengklasifikasi regresi logistik menghasilkan akurasi yang paling tinggi (77%) dibanding dengan Random Forest (68%), Decision Tree (55%), Adaboost (67%), dan K-Nearest Neighbour (65%) [4]. Selain itu, penelitian sebelumnya menunjukkan bahwa regresi logistik lebih superior dibanding algoritma pembelajaran mesin yang lain dalam hal pemodelan prediksi klinis [5].…”

Section: Abstract: Retinopathy Diabetic Logistic Regression Classificunclassified

Deteksi Diabetik Retinopati menggunakan Regresi Logistik

Tyasnurita¹,

Pamungkas²

2020

Ilk. J. Ilm.

View full text Add to dashboard Cite

Diabetes melitus merupakan penyakit metabolik dimana penderita diabetes tidak bisa memproduksi insulin dalam jumlah cukup atau tubuh tidak mampu menggunakan insulin secara efektif sehingga terjadi kelebihan gula di dalam darah. Kondisi ini baru dirasakan setelah terjadi komplikasi lanjut pada organ tubuh. Pada penderita diabetes melitus, dapat terjadi komplikasi pada tingkat sel dan anatomik. Komplikasi kronik yang disebabkan oleh diabetes melitus dapat terjadi pada pembuluh darah besar (makrovaskuler) dan pembuluh darah kecil (mikrovaskuler). Komplikasi makrovaskuler yang umum terjadi adalah pembekuan darah pada otak, penyakit jantung koroner, dan stroke. Sedangkan komplikasi mikrovaskuler yang umum terjadi adalah hiperglikemia dan retinopati (kebutaan) [1]. Retinopati diabetik merupakan kerusakan retina yang diakibatkan oleh komplikasi dari diabetes melitus [2]. Menurut data dari WHO pada tahun 2015, Indonesia menempati posisi ke tujuh di dunia untuk prevalensi penderita diabetes tertinggi di dunia dengan estimasi penderita diabetes sebesar 10 juta orang dari jumlah penderita diabetes di dunia sebesar 415 juta jiwa. Berdasarkan penelitian yang dilakukan oleh INFORMASI ARTIKEL

show abstract

An Ensemble based Machine Learning model for Diabetic Retinopathy Classification

Cited by 113 publications

References 33 publications

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification

Deteksi Diabetik Retinopati menggunakan Regresi Logistik

Contact Info

Product

Resources

About