Browsing by Author "Krishnan, A.S."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A Transfer Learning Approach for Diabetic Retinopathy Classification Using Deep Convolutional Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2018) Krishnan, A.S.; Clive, D.R.; Bhat, V.; Ramteke, P.B.; Koolagudi, S.G.
    Diabetic Retinopathy is a disease in which the retina is damaged due to diabetes mellitus. It is a leading cause for blindness today. Detection and quantification of such mellitus from retinal images is tedious and requires expertise. In this paper, an automatic identification of severity of Diabetic Retinopathy using Convolutional Neural Networks (CNNs) with a transfer learning approach has been proposed to aid the diagnostic process. A comparison of different CNN architectures such as ResNet, Inception-ResNet-v2 etc. is done using the quadratic weighted kappa metric. The qualitative and quantitative evaluation of the proposed approach is carried out on the Diabetic Retinopathy detection dataset from Kaggle. From the results, we observe that the proposed model achieves a kappa score of 0.76. © 2018 IEEE.
  • No Thumbnail Available
    Item
    SolveIt: An Application for Automated Recognition and Processing of Handwritten Mathematical Equations
    (Institute of Electrical and Electronics Engineers Inc., 2018) Sagar Bharadwaj, K.S.; Bhat, V.; Krishnan, A.S.
    Solving mathematical equations is an integral part of most, if not all forms of scientific studies. Researchers usually go through an arduous process of learning the nuances and syntactic complexities of a mathematical tool in order to solve or process mathematical equations. In this paper, we present a mobile application that can process an image of a handwritten mathematical equation captured using the device's camera, recognise the equation, form the corresponding string that can be parsed by a computer algebraic system and display all possible solutions. We aim to make the whole experience of experimenting with equations very user friendly and to remove the hassle of learning a mathematical tool just for mathematical experimentation. We propose a novel machine learning approach to recognise handwritten mathematical symbols achieving a 99.2% cross validation percentage accuracy on the kaggle math symbol dataset with reduced symbols. The application covers useful features like simultaneous equation solving, graph plotting and simple arithmetic computations from images. Overall it is a very user friendly equation solver that can leverage the power of existing powerful math packages. © 2018 IEEE.