RESEARCH ARTICLE
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A deep learning framework for glaucoma detection based on robust optic disc segmentation and transfer learning
Deepa Natarajan,
Esakkirajan Sankaralingam,
Keerthiveena Balraj,
Selvakumar Karuppusamy,
Corresponding Author
Deepa Natarajan
Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India
Correspondence
Deepa Natarajan, Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India.
Email: [email protected]
Search for more papers by this authorEsakkirajan Sankaralingam
Department of Instrumentation and Control Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Search for more papers by this authorKeerthiveena Balraj
Department of Instrumentation and Control Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Search for more papers by this authorSelvakumar Karuppusamy
School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Search for more papers by this authorDeepa Natarajan,
Esakkirajan Sankaralingam,
Keerthiveena Balraj,
Selvakumar Karuppusamy,
Corresponding Author
Deepa Natarajan
Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India
Correspondence
Deepa Natarajan, Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India.
Email: [email protected]
Search for more papers by this authorEsakkirajan Sankaralingam
Department of Instrumentation and Control Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Search for more papers by this authorKeerthiveena Balraj
Department of Instrumentation and Control Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Search for more papers by this authorSelvakumar Karuppusamy
School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Search for more papers by this authorAbstract
Glaucoma is a common ocular disorder, inflicting blindness in millions of people, early detection of which can reduce adverse outcomes. This paper presents a two-stage deep learning framework UNet-SNet, for glaucoma detection. Initially, each fundus image is segmented into GMM super pixels and the Region of Interest (RoI) is separated by Cuckoo Search Optimization (CSO). In the first stage, a regularized UNet is trained with RoIs for OD segmentation. In the second stage, a SqueezeNet is fine-tuned with deep features of the ODs to discriminate fundus images into glaucomotousor Normal. The UNet is trained and tested with the RIGA and RIM-ONEv2 datasets, achieving 97.84% and 99.85% accuracies respectively. The classifier is trained with the ODs segmented from the RIM-ONEv2 dataset and tested with ACRIMA, Drishti-GS1 and RIM-ONEv1 datasets accomplishing 99.86%, 97.05% and 100% accuracies respectively. Performance evaluations and complexity analyses with state-of-the-art systems demonstrate the superiority of the proposed model.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available in RIGA at https://doi.org/10.1117/12.2293584, reference number 29, RIM-ONE at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5999143&tag=1, reference number [30] and Drishti-gs at https://cvit.iiit.ac.in/projects/mip/drishti-gs/mip-dataset2/Home.php, reference number [31].
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