Iris Based Medical Diagnosis
Document Type
Oral Presentation
Campus where you would like to present
Ellensburg
Event Website
https://digitalcommons.cwu.edu/source
Start Date
16-5-2019
End Date
16-5-2019
Abstract
Automatic medical diagnosis without the involvement of trained medical personnel is a game changer in modern medicine. However, the implementation of these “computer doctors” is still in an early stage and there is a long process ahead until it will be accepted by the medical community. Our research is focusing on building a completely automatic tool to identify different eye pathologies by analyzing the irises of humans. The application we built can analyze, recognize, and identify different ocular diseases using as input human iris images such as geometry issues, occlusions, tissue and last but not least healthy eyes. First, the software segments the iris part of the eye from the input image which is later involved in a recognition phase considering several machine learning strategies. For our research experiments we considered different complex convolusional neural networks, namely the VGG19, the pretrained Xception, and the pretrained InceptionV3 model. To evaluate the accuracy of our method the Warsaw BioBase Disease Iris Database v2.1 was considered. The obtained results are very promising and an overall accuracy of 76% was achieved to separate healthy iris images from the non healthy ones.
Recommended Citation
Alsubhi, Jumana; Ortela, Raylison; Sullivan, Sean; Suzue, Nobukoni; and Ul Haq, Burhan, "Iris Based Medical Diagnosis" (2019). Symposium Of University Research and Creative Expression (SOURCE). 176.
https://digitalcommons.cwu.edu/source/2019/Oralpres/176
Department/Program
Computer Science
Iris Based Medical Diagnosis
Ellensburg
Automatic medical diagnosis without the involvement of trained medical personnel is a game changer in modern medicine. However, the implementation of these “computer doctors” is still in an early stage and there is a long process ahead until it will be accepted by the medical community. Our research is focusing on building a completely automatic tool to identify different eye pathologies by analyzing the irises of humans. The application we built can analyze, recognize, and identify different ocular diseases using as input human iris images such as geometry issues, occlusions, tissue and last but not least healthy eyes. First, the software segments the iris part of the eye from the input image which is later involved in a recognition phase considering several machine learning strategies. For our research experiments we considered different complex convolusional neural networks, namely the VGG19, the pretrained Xception, and the pretrained InceptionV3 model. To evaluate the accuracy of our method the Warsaw BioBase Disease Iris Database v2.1 was considered. The obtained results are very promising and an overall accuracy of 76% was achieved to separate healthy iris images from the non healthy ones.
https://digitalcommons.cwu.edu/source/2019/Oralpres/176
Faculty Mentor(s)
Szilard Vajda