Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods

• Published in: Computational and mathematical methods in medicine Vol. 2021; pp. 7666365 - 11
• Main Authors: Hasan, Md Kamrul, Tanha, Tanjum, Amin, Md Ruhul, Faruk, Omar, Khan, Mohammad Monirujjaman, Aljahdali, Sultan, Masud, Mehedi
• Format: Journal Article
• Language: English
• Published: United States Hindawi 08.12.2021
• Subjects: Algorithms; Cataract - classification; Cataract - diagnostic imaging; Computational Biology; Databases, Factual - statistics & numerical data; False Negative Reactions; False Positive Reactions; Humans; Image Interpretation, Computer-Assisted - methods; Image Interpretation, Computer-Assisted - statistics & numerical data; Machine Learning; Neural Networks, Computer; Photography - statistics & numerical data
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2021/7666365

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.