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Cataract Eye Prediction using Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 35 |
| Year of Publication: 2020 |
| Authors: Shruthi Bhat, Som Mosalagi, Tejal Bhalerao, Pushpak Katkar, Rahul Pitale |
10.5120/ijca2020920441
|
Shruthi Bhat, Som Mosalagi, Tejal Bhalerao, Pushpak Katkar, Rahul Pitale . Cataract Eye Prediction using Machine Learning. International Journal of Computer Applications. 176, 35 ( Jul 2020), 46-48. DOI=10.5120/ijca2020920441
Abstract
Humans, being visually oriented, witness the happenings in the surroundings with the help of eyes. In the current scenario, blindness, and visual impairment has become a major and ubiquitous health problem. Although new technologies are rapidly progressing, visual impairment remains a noteworthy problem for worldwide healthcare systems. One of such problems is Cataract. Cataracts causing poor vision may also result in an increased risk of falling and depression. Earlier, it was usual among old age people, but now childhood cataract has become an important cause of blindness and severe visual impairment in children. Existing studies have been done mostly on Fundus image datasets for automatic detection of cataract and grading using a predefined feature set. The challenge is to detect cataract using the normal lens images at an early stage thus allowing people to test for cataract themselves. This would rather ensure that people belonging to remote areas need not reach out to ophthalmologists, just to check whether the person is facing a cataract problem or not. This paper uses CNN models taking normal lens image input for detection of cataract problems.
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