Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model
Glaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/12/2497 |
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| author | Ramgopal Kashyap Rajit Nair Syam Machinathu Parambil Gangadharan Miguel Botto-Tobar Saadia Farooq Ali Rizwan |
| author_facet | Ramgopal Kashyap Rajit Nair Syam Machinathu Parambil Gangadharan Miguel Botto-Tobar Saadia Farooq Ali Rizwan |
| author_sort | Ramgopal Kashyap |
| collection | DOAJ |
| description | Glaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that are still building enough healthcare infrastructure to cope with glaucoma, the ailment is misdiagnosed nine times out of ten. To aid in the early diagnosis of glaucoma, the creation of a detection system is necessary. In this work, the researchers propose using a technology known as deep learning to identify and predict glaucoma before symptoms appear. The glaucoma dataset is used in this deep learning algorithm that has been proposed for analyzing glaucoma images. To get the required results when using deep learning principles for the job of segmenting the optic cup, pretrained transfer learning models are integrated with the U-Net architecture. For feature extraction, the DenseNet-201 deep convolution neural network (DCNN) is used. The DCNN approach is used to determine whether a person has glaucoma. The fundamental goal of this line of research is to recognize glaucoma in retinal fundus images, which will aid in assessing whether a patient has the condition. Because glaucoma can affect the model in both positive and negative ways, the model’s outcome might be either positive or negative. Accuracy, precision, recall, specificity, the F-measure, and the F-score are some of the metrics used in the model evaluation process. An extra comparison study is performed as part of the process of establishing whether the suggested model is accurate. The findings are compared to convolution neural network classification methods based on deep learning. When used for training, the suggested model has an accuracy of 98.82 percent and an accuracy of 96.90 percent when used for testing. All assessments show that the new paradigm that has been proposed is more successful than the one that is currently in use. |
| first_indexed | 2024-03-09T16:22:40Z |
| format | Article |
| id | doaj.art-6b1960241ff34a15b369b3a3ddf271a5 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T16:22:40Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-6b1960241ff34a15b369b3a3ddf271a52023-11-24T15:11:05ZengMDPI AGHealthcare2227-90322022-12-011012249710.3390/healthcare10122497Glaucoma Detection and Classification Using Improved U-Net Deep Learning ModelRamgopal Kashyap0Rajit Nair1Syam Machinathu Parambil Gangadharan2Miguel Botto-Tobar3Saadia Farooq4Ali Rizwan5Amity School of Engineering and Technology, Amity University Chhattisgarh, Raipur 493225, IndiaSchool of Computing Science and Engineering, VIT Bhopal University, Bhopal 466114, IndiaThe Home Depot, 5508 Ivy Summit Ct, Cumming, GA 30041, USADepartment of Mathematics and Computer Science, Eindhoven University of Technology, 5600 MB Eindhoven, The NetherlandsDepartment of Ophthalmology, Shifa College of Medicine (SCM), Shifa International Hospital, Islamabad 44000, PakistanDepartment of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaGlaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that are still building enough healthcare infrastructure to cope with glaucoma, the ailment is misdiagnosed nine times out of ten. To aid in the early diagnosis of glaucoma, the creation of a detection system is necessary. In this work, the researchers propose using a technology known as deep learning to identify and predict glaucoma before symptoms appear. The glaucoma dataset is used in this deep learning algorithm that has been proposed for analyzing glaucoma images. To get the required results when using deep learning principles for the job of segmenting the optic cup, pretrained transfer learning models are integrated with the U-Net architecture. For feature extraction, the DenseNet-201 deep convolution neural network (DCNN) is used. The DCNN approach is used to determine whether a person has glaucoma. The fundamental goal of this line of research is to recognize glaucoma in retinal fundus images, which will aid in assessing whether a patient has the condition. Because glaucoma can affect the model in both positive and negative ways, the model’s outcome might be either positive or negative. Accuracy, precision, recall, specificity, the F-measure, and the F-score are some of the metrics used in the model evaluation process. An extra comparison study is performed as part of the process of establishing whether the suggested model is accurate. The findings are compared to convolution neural network classification methods based on deep learning. When used for training, the suggested model has an accuracy of 98.82 percent and an accuracy of 96.90 percent when used for testing. All assessments show that the new paradigm that has been proposed is more successful than the one that is currently in use.https://www.mdpi.com/2227-9032/10/12/2497deep convolution neural networkimproved U-Netimage segmentationclassificationDenseNet-201 model |
| spellingShingle | Ramgopal Kashyap Rajit Nair Syam Machinathu Parambil Gangadharan Miguel Botto-Tobar Saadia Farooq Ali Rizwan Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model Healthcare deep convolution neural network improved U-Net image segmentation classification DenseNet-201 model |
| title | Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
| title_full | Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
| title_fullStr | Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
| title_full_unstemmed | Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
| title_short | Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
| title_sort | glaucoma detection and classification using improved u net deep learning model |
| topic | deep convolution neural network improved U-Net image segmentation classification DenseNet-201 model |
| url | https://www.mdpi.com/2227-9032/10/12/2497 |
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