An intelligible deep convolution neural network based approach for classification of diabetic retinopathy

• Autorzy: Sharma S. , Maheshwari S. , Shukla A.

Identyfikatory

DOI

10.1515/bams-2018-0011

• Języki publikacji: EN

Abstrakty

EN

Deep convolution neural networks (CNNs) have demonstrated their capabilities in modern-day medical image classification and analysis. The vital edge of deep CNN over other techniques is their ability to train without expert knowledge. Time bound detection is very beneficial for the early cure of disease. In this paper, a deep CNN architecture is proposed to classify nondiabetic retinopathy and diabetic retinopathy fundus eye images. Kaggle 2015 diabetic retinopathy competition dataset and messier experiment dataset are used in this study. The proposed deep CNN algorithm produces significant results with 93% area under the curve (AUC) for the Kaggle dataset and 91% AUC for the Messidor dataset. The sensitivity and specificity for the Kaggle dataset are 90.22% and 85.13%, respectively; the corresponding values of the Messidor dataset are 91.07% and 80.23%, respectively. The results outperformed many existing studies. The present architecture is a promising tool for diabetic retinopathy image classification.

Słowa kluczowe

EN

deep convolution neural network; diabetic retinopathy; image classification; image processing; machine learning

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2018
• Tom: Vol. 14, no. 2
• Strony: art. no. 20180011
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Sharma S.

• ABV-Indian Institute of Information Technology and Management, Gwalior, 474015, Madhya Pradesh, India

autor

Maheshwari S.

• ABV-Indian Institute of Information Technology and Management, Gwalior, 474015, Madhya Pradesh, India

autor

Shukla A.

• ABV-Indian Institute of Information Technology and Management, Gwalior, 474015, Madhya Pradesh, India

Bibliografia

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• [15] Andonová M, Pavlovicˇová J, Kajan S, Oravec M, Kurilová V. Diabetic retinopathy screening based on CNN. 2017 International Symposium ELMAR, Zadar, Croatia; 2017.
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• [17] Kaggle diabetic retinopathy detection competition. Available at: https://www.kaggle.com/c/diabetic-retinopathy-detection. Accessed: 30 Jun 2017.
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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).