A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset

Article Engineering, Electrical & Electronic

Automatic Diagnosis of Different Grades of Diabetic Retinopathy and Diabetic Macular Edema Using 2-D-FBSE-FAWT

Pradeep Kumar Chaudhary et al.

Summary: The study utilizes the 2-D-FBSE-FAWT method for diagnosing different grades of diabetic retinopathy and diabetic macular edema, evaluating using statistical features and various classification algorithms, and comparing performance through the ACY(avg) measure.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

Classification for diabetic retinopathy by using staged convolutional neural network

Hongqiu Wang et al.

Summary: Diabetic retinopathy (DR) is a common complication of diabetes and the leading cause of permanent blindness in the working-age population. Automatic grading approaches are crucial for helping ophthalmologists design adequate treatment to patients. Our proposed method achieves state-of-the-art results on the DDR dataset by utilizing a two-stage classification process and a lesion attention module.

2022 ASIA CONFERENCE ON ALGORITHMS, COMPUTING AND MACHINE LEARNING (CACML 2022) (2022)

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Article Computer Science, Information Systems

Automatic Severity Classification of Diabetic Retinopathy Based on DenseNet and Convolutional Block Attention Module

Mohamed M. Farag et al.

Summary: This paper introduces a new deep-learning-based approach for automatic severity detection using a single Color Fundus photograph (CFP) and trains the model on the Kaggle Asia Pacific Tele-Ophthalmology Society's (APTOS) dataset. The method achieves excellent performance in binary classification tasks and severity grading, reducing time and space complexity, making it a promising candidate for autonomous diagnosis.

IEEE ACCESS (2022)

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Proceedings Paper Engineering, Biomedical

Improved Automatic Grading of Diabetic Retinopathy Using Deep Learning and Principal Component Analysis

Eman Mohamed et al.

Summary: The study developed an automated deep learning-based approach for diabetic retinopathy multi-class detection and classification, evaluating the contribution of DR features in different color channels, selecting significant channels, and utilizing PCA for deep learning model training, achieving significant improvement in DR multi-class classification with 85% accuracy, 89% sensitivity, and 96% specificity.

2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) (2021)

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Article Computer Science, Interdisciplinary Applications