Academic Journal
Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods
| العنوان: | Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods |
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| المؤلفون: | Md Kamrul Hasan, Tanjum Tanha, Md Ruhul Amin, Omar Faruk, Mohammad Monirujjaman Khan, Sultan Aljahdali, Mehedi Masud |
| المصدر: | Computational and Mathematical Methods in Medicine, Vol 2021 (2021) |
| بيانات النشر: | Hindawi Limited |
| سنة النشر: | 2021 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main symptoms of this condition. These symptoms frequently result in difficulty doing a variety of tasks. As a result, preliminary cataract detection and prevention may help to minimize the rate of blindness. This paper is aimed at classifying cataract disease using convolutional neural networks based on a publicly available image dataset. In this observation, four different convolutional neural network (CNN) meta-architectures, including InceptionV3, InceptionResnetV2, Xception, and DenseNet121, were applied by using the TensorFlow object detection framework. By using InceptionResnetV2, we were able to attain the avant-garde in cataract disease detection. This model predicted cataract disease with a training loss of 1.09%, a training accuracy of 99.54%, a validation loss of 6.22%, and a validation accuracy of 98.17% on the dataset. This model also has a sensitivity of 96.55% and a specificity of 100%. In addition, the model greatly minimizes training loss while boosting accuracy. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1748-6718 |
| Relation: | http://dx.doi.org/10.1155/2021/7666365; https://doaj.org/toc/1748-6718; https://doaj.org/article/e8035fa6eb494919a617a38374f5b80a |
| DOI: | 10.1155/2021/7666365 |
| الاتاحة: | https://doi.org/10.1155/2021/7666365 https://doaj.org/article/e8035fa6eb494919a617a38374f5b80a |
| رقم الانضمام: | edsbas.36A640FF |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 17486718 |
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| DOI: | 10.1155/2021/7666365 |