Abstract
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.
Highlights
A cataract is a type of eye disease where the eyes look cloudy
Recent cases of cataract increased by 43.6%, with nuclear cataracts accounting for 23.1%, Posterior Subcapsular Cataracts (PSC) for 13.1%, and cortical cataracts for 22%, and cataract surgery was done only for 26.8%
This paper presents the comparison of the performances of four distinct deep learning models, namely, DenseNet121, Xception, InceptionV3, and InceptionResNetV2, on training, validation, and test datasets for cataract disease detection
Summary
A cataract is a type of eye disease where the eyes look cloudy. A person with cataracts will have frosty or fogged-up vision. According to the 1998 World Health Report, 19.34 million people are blind bilaterally (less than 3/60 in the better eye) as a result of age-related cataracts. This accounted for 43% of all blindness cases [3]. Recent cases of cataract increased by 43.6%, with nuclear cataracts accounting for 23.1%, Posterior Subcapsular Cataracts (PSC) for 13.1%, and cortical cataracts for 22%, and cataract surgery was done only for 26.8%. All types of cataract surgery have increased in recent years. Studies show that there are more female patients compared to males This includes nuclear and cortical cataracts and cataract surgery (p = 0:02 – 0:05).
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