Home / Journals International Journal of Ophthalmology & Visual Science / Computational Intelligence for Visual Disease Detection
Computational Intelligence for Visual Disease Detection
Submission Deadline: Feb. 20, 2020

This special issue currently is open for paper submission and guest editor application.

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Lead Guest Editor
Azhar Imran
School of Software Engineering, Beijing University of Technology, Beijing, China
Guest Editors
  • Azhar Mushtaq
    Department of Computer Science, University of Sargodha, Sargodha, Pakistan
  • Jahanzaib Latif
    Department of Computer Science, Beijing University of Technology, Beijing, China
  • Muhammad Faiyaz
    Computer Science & Information Technology Department, Quaid-e-Azam University, Islamabad, Pakistan
  • Anas Bilal
    Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • Rehan Akram
    Faculty of Software Engineering, Hamdard University, Islamabad, Pakistan
  • Shazia Yousaf
    Department of Computer Science, International Islamic University, Islamabad, Pakistan
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=230). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.

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Special Issue

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.
Artificial Neural Networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog.
A deep learning system (DLS) uses artificial intelligence and representation learning methods to process large data and extract meaningful patterns. A few DLSs have recently shown high sensitivity and specificity (>90%) in detecting referable diabetic retinopathy from retinal photographs, primarily using high-quality images from publicly available databases from homogenous populations of white individuals. The performance of a DLS in screening for diabetic retinopathy should ideally be evaluated in clinical or population settings in which retinal images from patients of different races and ethnicities (and therefore with varying fundi pigmentation) have varying qualities (e.g., due to poor pupil dilation, media opacity, poor contrast or focus). Furthermore, in screening programs for diabetic retinopathy, the detection of incidental but related vision-threatening eye diseases, such as glaucoma and age-related macular degeneration (AMD), should be incorporated because missing such cases is clinically unacceptable.
Aims and Scope:
  1. Retinal Disease Detection
  2. Retinal Imaging
  3. Cataract, Glaucoma, Diabetic Retinopathy Detection and Grading
  4. Retinal Image Classification
  5. Identification and Classification of Ophthalmic Diseases
  6. Detection of Visual Diseases from Medical Images
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