Identification and Classification of Cataract Stages in Old Age People Using Deep Learning Algorithm
Sahana M1, Gowrishankar S2
1Sahana M, Department of Computer Science and Engineering, Dr. Ambedkar Institute of Technology, Bengaluru, India.
2Gowrishankar S, Department of Computer Science and Engineering, Dr. Ambedkar Institute of Technology, Bengaluru, India.
Manuscript received on 07 August 2019 | Revised Manuscript received on 14 August 2019 | Manuscript published on 30 August 2019 | PP: 2767-2772 | Volume-8 Issue-10, August 2019 | Retrieval Number: J95820881019/2019©BEIESP | DOI: 10.35940/ijitee.J9582.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cataract is a dense cloudy area that forms in a lens of the eye because of which many people are going blind. More than 50% of people in old age suffer due to cataract and will not have a clear vision. In the convolutional neural network, there are many trained models which help in the classification of the object. We use transfer learning technology to train the model for the data set we have. The image feature extraction model with the inception V3 architecture trained on image net. Cataract and normal image dataset are collected. A cataract is further divided into a mature and immature cataract. The result shows whether the image is either a normal eye or cataract eye with the model accuracy being 87.5%. If in the presence of cataract, the model will identify the stage of cataract.
Keywords: Cataract, Conventional Neural Network, Tensorflow, Transfer Learning, Inception V3.
Scope of the Article: Classification