Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis

The first diagnosis of diabetic retinopathy (DR) must include lesion segmentation. As it takes a lot of time and effort to label lesions, automatic segmentation methods have to be created manually. The degree of the retina's degenerative lesions determines how severe diabetic retinopathy is. A...

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Veröffentlicht in:	IEEE access Jg. 11; S. 70853 - 70864
Hauptverfasser:	Kumar, B. Naveen, Mahesh, T. R., Geetha, G., Guluwadi, Suresh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Abnormalities Algorithms Artificial neural networks Coders convolutional neural network Convolutional neural networks Decoding Deep learning Diabetes Diabetic retinopathy Diseases encoder-decoder network Encoders-Decoders Encoding Exudation fundus images Image analysis Image enhancement Image segmentation lesion segmentation Lesions Machine learning Network analysis Neural networks patch generation Retina spatial attention mechanism
ISSN:	2169-3536, 2169-3536
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Abstract	The first diagnosis of diabetic retinopathy (DR) must include lesion segmentation. As it takes a lot of time and effort to label lesions, automatic segmentation methods have to be created manually. The degree of the retina's degenerative lesions determines how severe diabetic retinopathy is. A major influence is on the early detection of illness and treatment of DR. To reliably identify the sites of related lesions and identify various abnormalities in retinal fundus pictures, deep learning algorithms are crucial. Additionally, utilizing patch-based analysis, a deep convolutional neural network is constructed. In this study, encoder-decoder neural networks along with channel-wise spatial Attention Mechanisms are proposed. The IDRiD dataset, which includes hard exudate segmentations, is used to train and evaluate the architecture. In this method, image patches are created using the sliding window technique. To determine the effectiveness of the recommended strategy, a thorough experiment was conducted on IDRiD. In order to predict the various sorts of lesions, the trained network analyses the picture patches and creates a probability map. This technique's efficacy and supremacy are confirmed by the expected accuracy of 99.94 %. The findings of this experiment show significantly enhanced performance in terms of accuracy when compared to prior research on comparable tasks.
AbstractList	The first diagnosis of diabetic retinopathy (DR) must include lesion segmentation. As it takes a lot of time and effort to label lesions, automatic segmentation methods have to be created manually. The degree of the retina's degenerative lesions determines how severe diabetic retinopathy is. A major influence is on the early detection of illness and treatment of DR. To reliably identify the sites of related lesions and identify various abnormalities in retinal fundus pictures, deep learning algorithms are crucial. Additionally, utilizing patch-based analysis, a deep convolutional neural network is constructed. In this study, encoder-decoder neural networks along with channel-wise spatial Attention Mechanisms are proposed. The IDRiD dataset, which includes hard exudate segmentations, is used to train and evaluate the architecture. In this method, image patches are created using the sliding window technique. To determine the effectiveness of the recommended strategy, a thorough experiment was conducted on IDRiD. In order to predict the various sorts of lesions, the trained network analyses the picture patches and creates a probability map. This technique's efficacy and supremacy are confirmed by the expected accuracy of 99.94 %. The findings of this experiment show significantly enhanced performance in terms of accuracy when compared to prior research on comparable tasks.
Author	Geetha, G. Guluwadi, Suresh Kumar, B. Naveen Mahesh, T. R.
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SubjectTerms	Abnormalities Algorithms Artificial neural networks Coders convolutional neural network Convolutional neural networks Decoding Deep learning Diabetes Diabetic retinopathy Diseases encoder-decoder network Encoders-Decoders Encoding Exudation fundus images Image analysis Image enhancement Image segmentation lesion segmentation Lesions Machine learning Network analysis Neural networks patch generation Retina spatial attention mechanism
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Title	Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis
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