A Detection of Amblyopia Medical Condition in Biomedical Datasets Using Image Segmentation and Detection Processing

Abstract

The recent past, the data volume in a media field is growing at a rapid rate, and conventional methods fail to manage such a large volume of data in healthcare systems, biomedical field, medical diagnostic systems etc. The main challenges associated with biomedical computation are the problems associated with management, storage, and analysis on extensive biomedical data. To play a significant role over such extensive data, the machine learning approach provides faster access to medical data with an improved framework. The main objective involves the detection of amblyopia condition from input images and comparing it with conventional image detection methods. The proposed method is examined in terms of detection accuracy, sensitivity, specificity, Hausdorff distance computation and Dice Coefficient. Also, the detection of an Amblyopic or Lazy Eye diseased images is still not prevalent in the field of image segmentation and detection. In this paper, we introduce a framework to process the Amblyopia image datasets using machine learning, and similarity comparison approach. The proposed image processing involves the segmentation of eye images using Recurrent Neural Networks (RNN), and the detection of Amblyopia disease is carried out with Hausdorff Distance computation and Dice coefficient similarity comparison on the segmented image. The initial subset points and threshold values are calculated from a set of 50 normal eye images. A set of 100 Amblyopic diseased image dataset is used for testing the proposed system, out of which 70 images are used for training the system. To evaluate the experimental results shows that proposed method obtains improved detection than existing Deeply-Learned Gaze Shifting Path (DLGSP), Cascade Regression Framework (CRF) and Mobile Iris Recognition System (MIRS) methods. The presence of Hausdorff Distance computation and Dice coefficient similarity comparison is used for reducing the overhead in the proposed method, and this can be used for computing large sets of images.