Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India

Research Article

Glaucoma and diabetic retinopathy diagnosis using image mining

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  • @INPROCEEDINGS{10.4108/eai.7-6-2021.2308568,
        author={B.V.  Baiju and V.Ceronmani  Sharmila and Muthuraj  B and Abdul  Hasan M and Mahendar  V},
        title={Glaucoma and diabetic retinopathy diagnosis using image mining},
        proceedings={Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India},
        publisher={EAI},
        proceedings_a={I3CAC},
        year={2021},
        month={6},
        keywords={diabetic retinopathy image processing feature extraction deep learning techniques},
        doi={10.4108/eai.7-6-2021.2308568}
    }
    
  • B.V. Baiju
    V.Ceronmani Sharmila
    Muthuraj B
    Abdul Hasan M
    Mahendar V
    Year: 2021
    Glaucoma and diabetic retinopathy diagnosis using image mining
    I3CAC
    EAI
    DOI: 10.4108/eai.7-6-2021.2308568
B.V. Baiju1,*, V.Ceronmani Sharmila1, Muthuraj B1, Abdul Hasan M1, Mahendar V1
  • 1: Department of Information Technology, Hindustan Institute of Technology and Sciences, Chennai
*Contact email: bvbaiju@hindustanuniv.ac.in

Abstract

Diabetes is an overall unavoidable sickness that can cause recognizable microvascular complexities like diabetic retinopathy and macular edema in the normal eye retina, the pictures of which are today used for manual disease screening and assurance. This work genuine task could inconceivably productive by customized acknowledgment using a Deep learning methodology. Here we present a profound learning structure that perceives referable diabetic retinopathy comparably or better than presented in the past investigations. The proposed strategy evades the need of sore division or applicant map age before the arrangement stage. Neighbourhood parallel examples and granulometric profiles are privately registered to extricate surface and morphological data from retinal images. Various blends of this data feed arrangement calculations to ideally separate brilliant and dark lesions from solid tissues. These outcomes propose, radial basis function in classification could build the expense viability of screening and finding, while at the same time accomplishing higher than suggested execution, and that the framework could be applied in clinical assessments requiring better reviewing.