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  1. Maheshwari S, Animasahun BA, Njokanma OF
    Indian Heart J, 2012 Jan-Feb;64(1):50-3.
    PMID: 22572426 DOI: 10.1016/S0019-4832(12)60011-X
    Factors that have led to the increasing popularity of medical travel include the high cost of healthcare, long wait times for certain procedures, the ease and affordability of international travel, and improvements in both technology and standards of care in many countries.
  2. Maheshwari S, Kanhangad V, Pachori RB, Bhandary SV, Acharya UR
    Comput Biol Med, 2019 Feb;105:72-80.
    PMID: 30590290 DOI: 10.1016/j.compbiomed.2018.11.028
    BACKGROUND AND OBJECTIVE: Glaucoma is a ocular disorder which causes irreversible damage to the retinal nerve fibers. The diagnosis of glaucoma is important as it may help to slow down the progression. The available clinical methods and imaging techniques are manual and require skilled supervision. For the purpose of mass screening, an automated system is needed for glaucoma diagnosis which is fast, accurate, and helps in reducing the burden on experts.

    METHODS: In this work, we present a bit-plane slicing (BPS) and local binary pattern (LBP) based novel approach for glaucoma diagnosis. Firstly, our approach separates the red (R), green (G), and blue (B) channels from the input color fundus image and splits the channels into bit planes. Secondly, we extract LBP based statistical features from each of the bit planes of the individual channels. Thirdly, these features from the individual channels are fed separately to three different support vector machines (SVMs) for classification. Finally, the decisions from the individual SVMs are fused at the decision level to classify the input fundus image into normal or glaucoma class.

    RESULTS: Our experimental results suggest that the proposed approach is effective in discriminating normal and glaucoma cases with an accuracy of 99.30% using 10-fold cross validation.

    CONCLUSIONS: The developed system is ready to be tested on large and diverse databases and can assist the ophthalmologists in their daily screening to confirm their diagnosis, thereby increasing accuracy of diagnosis.

  3. Maheshwari S, Pachori RB, Kanhangad V, Bhandary SV, Acharya UR
    Comput Biol Med, 2017 Sep 01;88:142-149.
    PMID: 28728059 DOI: 10.1016/j.compbiomed.2017.06.017
    Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.
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