Abstract:
Being one of the most serious causes of irreversible blindness, all over the world, glaucoma is the asymptomatic disease which produces damage at the optic nerve head lev...Show MoreMetadata
Abstract:
Being one of the most serious causes of irreversible blindness, all over the world, glaucoma is the asymptomatic disease which produces damage at the optic nerve head level. The symptoms related to glaucoma are not noticeable until severe stages of this illness and they appear when the patient has already lost a significant part of his eyesight. As the detection of glaucoma is vital for preventing blindness, regular eye investigations have to be made. However, these examinations might not be relevant for glaucoma diagnosis. Nonetheless, with the continuous development of deep learning algorithms, early detection of glaucoma based on digital fundus images, is possible. As convolutional neural networks (CNNs) have proven good performance in early detection of several diseases, they have been recently used in the ophthalmological field for the identification of certain eye illnesses, including glaucoma. This paper proposes a new method which uses densely connected neural networks (DenseNet) with 201 layers, initially pre-trained on ImageNet, using ACRIMA dataset. An accuracy of approximately 97% and an f1-score of 0.969 were obtained which gives us cause for careful optimism concerning the usefulness of this classification model for the early detection of glaucoma.
Published in: 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)
Date of Conference: 01-03 July 2021
Date Added to IEEE Xplore: 23 August 2021
ISBN Information:
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- IEEE Keywords
- Index Terms
- Deep Learning ,
- Fundus Images ,
- Glaucoma Detection ,
- Digital Fundus Images ,
- Neural Network ,
- Convolutional Network ,
- Convolutional Neural Network ,
- Digital Images ,
- Ophthalmology ,
- F1 Score ,
- Optic Nerve ,
- Optic Nerve Head ,
- Cause Of Irreversible Blindness ,
- Visual Impairment ,
- Convolutional Layers ,
- Linear Discriminant Analysis ,
- Confusion Matrix ,
- Data Augmentation ,
- Precision And Recall ,
- Intraocular Pressure ,
- Optic Cup ,
- Vertical Flip ,
- Dense Block ,
- Data Augmentation Techniques ,
- Convolutional Block ,
- Convolutional Neural Network Model ,
- Present Method ,
- Independent Component Analysis ,
- Glaucoma Severity ,
- Area Under Curve
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Deep Learning ,
- Fundus Images ,
- Glaucoma Detection ,
- Digital Fundus Images ,
- Neural Network ,
- Convolutional Network ,
- Convolutional Neural Network ,
- Digital Images ,
- Ophthalmology ,
- F1 Score ,
- Optic Nerve ,
- Optic Nerve Head ,
- Cause Of Irreversible Blindness ,
- Visual Impairment ,
- Convolutional Layers ,
- Linear Discriminant Analysis ,
- Confusion Matrix ,
- Data Augmentation ,
- Precision And Recall ,
- Intraocular Pressure ,
- Optic Cup ,
- Vertical Flip ,
- Dense Block ,
- Data Augmentation Techniques ,
- Convolutional Block ,
- Convolutional Neural Network Model ,
- Present Method ,
- Independent Component Analysis ,
- Glaucoma Severity ,
- Area Under Curve
- Author Keywords