Smart Training of Alex Net using Fluorescein Angiography Fundus Images Implementing Selective Data Sampling
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2021
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| _version_ | 1866901161815048192 |
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| author | Shubham Chaudhary Vipul Sharma Akshay Khandelwal |
| author_facet | Shubham Chaudhary Vipul Sharma Akshay Khandelwal |
| contents | This research paper pertains to the concept of preliminary detection of Posterior Capsular Opacification, Glaucoma and Genetic Diseases in the human eye using Alex Net, which is one of the earliest CNN found in the MATLAB. CNN are trained with specific layers using the given dataset taking blood vessels as the parameter for detecting pre-abnormalities from the images. The specific layers are used for reducing the time for training and detection. The layers have been chosen based on the different parameters like filters, rate of training, initial rate, statistical parameters and mathematical modelling. After this the system is trained for 100 to 1000 epochs in which the test image is chosen as input and is compared with disease affected and normal blood vessels. It is found that for first training the system had a detection with an accuracy of 50 to 60% and with subsequent training it had an efficiency of 80-90% |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18629216 |
| institution | Zenodo |
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| publishDate | 2021 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Smart Training of Alex Net using Fluorescein Angiography Fundus Images Implementing Selective Data Sampling Shubham Chaudhary Vipul Sharma Akshay Khandelwal Posterior Capsular Opacification Convolutional Neural Network ALEXNET Regression-statistical model used in CNN Sampling fundus. This research paper pertains to the concept of preliminary detection of Posterior Capsular Opacification, Glaucoma and Genetic Diseases in the human eye using Alex Net, which is one of the earliest CNN found in the MATLAB. CNN are trained with specific layers using the given dataset taking blood vessels as the parameter for detecting pre-abnormalities from the images. The specific layers are used for reducing the time for training and detection. The layers have been chosen based on the different parameters like filters, rate of training, initial rate, statistical parameters and mathematical modelling. After this the system is trained for 100 to 1000 epochs in which the test image is chosen as input and is compared with disease affected and normal blood vessels. It is found that for first training the system had a detection with an accuracy of 50 to 60% and with subsequent training it had an efficiency of 80-90% |
| title | Smart Training of Alex Net using Fluorescein Angiography Fundus Images Implementing Selective Data Sampling |
| topic | Posterior Capsular Opacification Convolutional Neural Network ALEXNET Regression-statistical model used in CNN Sampling fundus. |
| url | https://doi.org/10.5281/zenodo.18629216 |