Pragmatic Analytics on Hybrid Computer Vision Models to Develop a Stable Framework for Visual Impairment – A Survey

Pragmatic Analytics on Hybrid Computer Vision Models to Develop a Stable Framework for Visual Impairment – A Survey

  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-9
Year of Publication : 2023
Author : S. Sajini, B. Pushpa
DOI : 10.14445/22315381/IJETT-V71I9P202

How to Cite?

S. Sajini, B. Pushpa, "Pragmatic Analytics on Hybrid Computer Vision Models to Develop a Stable Framework for Visual Impairment – A Survey," International Journal of Engineering Trends and Technology, vol. 71, no. 9, pp. 11-26, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I9P202

Abstract
Computer vision is an expertise associated with artificial intelligence and image processing arenas to excerpt meaningful information from visual components like images and videos. Computer vision finds chief implications in solving pragmatic complications in the real world, like visual impairment. The major objective of the paper is to conduct a pragmatic survey on the various hybrid computer vision Models with other thrust areas like image processing, artificial intelligence, and machine learning that could be used to develop a proposed stable framework to solve the problem of visual impairment. The paper presented an expounded survey on existing models for visual impairment and its existing algorithms. The work continues with a comprehensive survey of various models and frameworks, announced over a period from 2015 to 2022. Based on the recognised models, comparative analytics has been accomplished to ponder the best models and frameworks from different research sources. After Review and Surveys, the Research gaps acknowledged in the prevailing models and frameworks were accessed. Finally, a glimpse of the proposed model for visual impairment was presented for emerging a stable framework in the looming future.

Keywords
Computer vision, Hybrid computer vision model, Visual impairment, CV Hybrid framework, Comparative analytics.

References
[1] Rohit Varma, Kristina Tarczy-Hornoch, and Xuejuan Jiang, "Visual Impairment in Preschool Children in the United States: Demographic and Geographic Variations from 2015 To 2060," JAMA Ophthalmology, vol. 135, no. 6, pp. 610-616, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Peter Ackland, Serge Resnikoff, and Rupert Bourne, "World Blindness and Visual Impairment: Despite Many Successes, the Problem is Growing," Community Eye Health, vol. 30, no. 100, pp. 71-73, 2017.
 [Google Scholar] [Publisher Link]
[3] Rohit Varma et al., "Visual Impairment and Blindness in Adults in the United States: Demographic and Geographic Variations from 2015 to 2050," JAMA Ophthalmology, vol. 134, no. 7, pp. 802-809, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Kristen A. Eckert et al., "A Simple Method for Estimating the Economic Cost of Productivity Loss due to Blindness and Moderate to Severe Visual Impairment," Ophthalmic Epidemiology, vol. 22, no. 5, pp. 349-355, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Antoine Gbessemehlan et al., "Association between Visual Impairment and Cognitive Disorders in Low-and-Middle Income Countries: A Systematic Review," Aging & Mental Health, vol. 25, no. 10, pp. 1786-1795, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Clarissa Ng Yin Ling et al., "Visual Impairment, Major Eye Diseases, and Mortality in a Multi-Ethnic Asian Population and a Meta-analysis of Prospective Studies," American Journal of Ophthalmology, vol. 231, pp. 88-100, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Brian L. DeCost, and Elizabeth A. Holm, "A Computer Vision Approach for Automated Analysis and Classification of Micro Structural Image Data," Computational Materials Science, vol. 110, pp. 126-133, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[8] JoonOh Seo et al., "Computer Vision Techniques for Construction Safety and Health Monitoring," Advanced Engineering Informatics, vol. 29, no. 2, pp. 239-251, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[9] N. V. Kartheek Medathati et al., "Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision," Computer Vision and Image Understanding, vol. 150, pp. 1-30, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Clayton R. Pereira et al., "A New Computer Vision-based Approach to Aid the Diagnosis of Parkinson's Disease," Computer Methods and Programs in Biomedicine, vol. 136, pp. 79-88, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Jérôme Thevenot, Miguel Bordallo López, and Abdenour Hadid, "A Survey on Computer Vision for Assistive Medical Diagnosis from Faces," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1497-1511, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] M. Leo et al., "Computer Vision for Assistive Technologies," Computer Vision and Image Understanding, vol. 154, pp. 1-15, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Junfeng Gao et al., "Computer Vision in Healthcare Applications," Journal of Health Care Engineering, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Qiuhong Ke et al., "Computer Vision for Human–Machine Interaction," Computer Vision for Assistive Healthcare, Academic Press, pp. 127-145, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] J. Harikrishnan et al., "Vision-Face Recognition Attendance Monitoring System for Surveillance using Deep Learning Technology and Computer Vision," International Conference on Vision towards Emerging Trends in Communication and Networking, IEEE, pp. 1-5, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Hao Wu, Deyang Wu, and Jinsong Zhao, "An Intelligent Fire Detection Approach through Cameras based on Computer Vision Methods," Process Safety and Environmental Protection, vol. 127, pp. 245-256, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Lev Manovich, "Computer Vision, Human Senses, and Language of Art," AI & SOCIETY, vol. 36, pp. 1145-1152, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Vitaliy Gargin et al., "Application of the Computer Vision System for Evaluation of Pathomorphological Images," IEEE 40th International Conference on Electronics and Nanotechnology, pp. 469-473, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Jing Jin et al., "A Feature Binding Model in Computer Vision for Object Detection," Multimedia Tools and Applications, vol. 80, no. 13, pp. 19377-19397, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Thomas M. Ward et al., "Computer Vision in Surgery," Surgery, vol. 169, no. 5, pp. 1253-1256, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Ronald Tombe, and Serestina Viriri, "Effective Processing of Convolutional Neural Networks for Computer Vision: A Tutorial and Survey," IETE Technical Review, vol. 39, no. 1, pp. 49-62, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Andrea Ghermandi, Yaella Depietri, and Michael Sinclair, "In the AI of the Beholder: A Comparative Analysis of Computer Vision-Assisted Characterizations of Human- Nature Interactions in Urban Green Spaces," Landscape and Urban Planning, vol. 217, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Hemad Zareiforoush et al., "A Hybrid Intelligent Approach based on Computer Vision and Fuzzy Logic for Quality Measurement of Milled Rice," Measurement, vol. 66, pp. 26-34, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[24] J.D. Tamayo-Quintero, S. Arboleda-Duque, and J.B. Gómez-Mendoza, "Semi-Automatic Teeth Segmentation in 3D Models of Dental Casts using a Hybrid Methodology," Emerging Trends in Image Processing, Computer Vision and Pattern Recognition, pp. 433-446, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Viachaslau Kachurka et al., "Visual Saliency based Approach to Object Detection in Computer Vision Systems: Real Life Applications," IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, pp. 239-244, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[26] T. Santha, and V. Mohana Maniganda Babu, "The Significance of Real-Time, Biomedical and Satellite Image Processing in Understanding the Objects & Application to Computer Vision," IEEE International Conference on Engineering and Technology, pp. 661-670, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Shruti Shukla, Ashish Lakhmani, and Ambuj Kumar Agarwal, "Approaches of Artificial Intelligence in Biomedical Image Processing: A Leading Tool between Computer Vision & Biological Vision," International Conference on Advances in Computing, Communication, & Automation, IEEE, pp. 1-6, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[28] S. Prathiba, and B. Sivagami, "Newfangled Applications of Digital Image Processing," SSRG International Journal of Computer Science and Engineering, vol. 6, no. 11, pp. 28-32, 2019.
[CrossRef] [Publisher Link]
[29] Peratham Wiriyathammabhum et al., "Computer Vision and Natural Language Processing: Recent Approaches in Multimedia and Robotics," ACM Computing Surveys, vol. 49, no. 4, pp. 1-44, 206.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Mu-Cyun Tang et al., "A Hybrid Computer Vision and Wi-Fi Doppler Radar System for Capturing the 3-D Hand Gesture Trajectory with A Smart Phone," IEEE MTT-S International Microwave Symposium, pp. 1251-1254, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Jun-Kit Chaw, and Musa Mokji, "Analysis of Produce Recognition System with Taxonomist's Knowledge using Computer Vision and Different Classifiers," IET Image Processing, vol. 11, no. 3, pp. 173-182, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Dandara T. G. Andrade et al., "A Hybrid Approach for the Actuation of Upper Limb Prostheses based on Computer Vision," Latin American Robotics Symposium, and 2017 Brazilian Symposium on Robotics (SBR) IEEE, pp. 1-6, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Diego Inácio Patrício, and Rafael Rieder, "Computer Vision and Artificial Intelligence in Precision Agriculture for Grain Crops: A Systematic Review," Computers and Electronics in Agriculture, vol. 153, pp. 69-81, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Eva María Artime Ríos et al., "A Hybrid Algorithm for the Prediction of Computer Vision Syndrome in Health Personnel based on Trees and Evolutionary Algorithms," International Conference on Hybrid Artificial Intelligence Systems, Springer, Cham, pp. 597-608, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Weili Fang et al., "Falls from Heights: A Computer Vision-Based Approach for Safety Harness Detection," Automation in Construction, vol. 91, pp. 53-61, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Niall O'Mahony et al., "Deep Learning Vs. Traditional Computer Vision," Science and Information Conference, Springer, Cham, pp.128-144, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Roos van der Donk et al., "Next-Generation Phenol Typing using Computer Vision Algorithms in Rare Genomic Neuro Developmental Disorders," Genetics in Medicine, vol. 21, pp. 1719-1725, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Changrui Chen et al., "RRNet: A Hybrid Detector for Object Detection in Drone-Captured Images," Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019.
 [Google Scholar] [Publisher Link]
[39] Ejaz Ul Haq et al., "A Fast Hybrid Computer Vision Technique for Real-Time Embedded Bus Passenger Flow Calculation through Camera," Multimedia Tools and Applications, vol. 79, no. 1, pp. 1007-1036, 2020.
[CrossRef] [Google Scholar] [Publisher Link]]
[40] Weili Fang et al., "Knowledge Graph for Identifying Hazards on Construction Sites: Integrating Computer Vision with Ontology," Automation in Construction, vol. 119, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Ipek Gursel Dino et al., "Image-Based Construction of Building Energy Models using Computer Vision," Automation in Construction, vol. 116, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Amol Mangrulkar, Santosh B. Rane, and Vivek Sunnapwar, "Automated Skull Damage Detection from Assembled Skull Model Using Computer Vision and Machine Learning," International Journal of Information Technology, vol. 13, no. 5, pp. 1785-1790, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Ai-Min Yang et al., "Computer Vision Technology based on Sensor Data and Hybrid Deep Learning for Security Detection of Blast Furnace Bearing," IEEE Sensors Journal, vol. 21, no. 22, pp. 24982-24992, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Siddharth Singh Chouhan et al., "Leaf Disease Segmentation and Classification of Jatropha Curcas L. and Pongamia Pinnata L. Biofuel Plants using Computer Vision-Based Approaches," Measurement, vol. 171, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Zhanxiong Ma et al., "Structural Displacement Estimation by Fusing Vision Camera and Accelerometer using Hybrid Computer Vision Algorithm and Adaptive Multi-Rate Kalman Filter," Automation in Construction, vol. 140, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Victor Rezende Franco et al., "Hybrid Machine Learning Methods Combined with Computer Vision Approaches to Estimate Biophysical Parameters of Pastures," Evolutionary Intelligence, vol. 16, pp. 1271-1284, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Hieu Nguyen, and Nhat-Duc Hoang, "Computer Vision-based Classification of Concrete Spall Severity using Metaheuristic-Optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network," Automation in Construction, vol. 140, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Hotaka Takizawa et al., "Kinect Cane: An Assistive System for the Visually Impaired based on the Concept of Object Recognition Aid," Personal and Ubiquitous Computing, vol. 19, no. 5, pp. 955-965, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[49] P. Priyadarshini et al., "Smart Backing Cane For Visually Impaired," SSRG International Journal of Computer Science and Engineering, vol. 7, no. 5, pp. 21-24, 2020.
[CrossRef] [Publisher Link]
[50] Jizhong Xiao et al., "An Assistive Navigation Framework for the Visually Impaired," IEEE Transactions on Human-Machine Systems, vol. 45, no. 5, pp. 635-640, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Namita Agarwal et al., "Electronic Guidance System for the Visually Impaired—A Framework," International Conference on Technologies for Sustainable Development, IEEE, pp. 1-5, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[52] Dejing Ni et al., "A Walking Assistant Robotic System for the Visually Impaired based on Computer Vision and Tactile Perception," International Journal of Social Robotics, vol. 7, no. 5, pp. 617-628, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[53] Manuela Chessa et al., "An Integrated Artificial Vision Framework for Assisting Visually Impaired Users," Computer Vision and Image Understanding, vol. 149, pp. 209-228, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[54] Sanket Khade, and Yogesh H. Dandawate, "Hardware Implementation of Obstacle Detection for Assisting Visually Impaired People in an Unfamiliar Environment by Using Raspberry Pi," International Conference on Smart Trends for Information Technology and Computer Communications, pp. 889-895, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Laurindo Britto Neto et al., “A Kinect-Based Wearable Face Recognition System to Aid Visually Impaired Users,” IEEE Transactions on Human-Machine Systems, vol. 47, no. 1, pp. 52-64, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[56] S. Senthamizhselvi, and A. Saravanan, "Visual Place Recognition Model using Deep Learning with Arithmetic Optimization Algorithm," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 7, pp. 74-86, 2023.
[CrossRef] [Publisher Link]
[57] Ruxandra Tapu, Bogdan Mocanu, and Titus Zaharia, "DEEP-SEE: Joint Object Detection, Tracking and Recognition with Application to Visually Impaired Navigational Assistance," Sensors, vol. 17, no. 11, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[58] Chan Kit Yan, Ulrich Engelke, and Nimsiri Abhayasinghe, "An Edge Detection Framework Conjoining with IMU Data for Assisting Indoor Navigation of Visually Impaired Persons," Expert Systems with Applications, vol. 67, pp. 272-284, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[59] John-Ross Rizzo et al., "Sensor Fusion for Ecologically Valid Obstacle Identification: Building a Comprehensive Assistive Technology Platform for the Visually Impaired," 7th International Conference on Modeling, Simulation, and Applied Optimization, IEEE, pp. 1-5, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[60] Charmi T. Patel et al., "Multi Sensor-Based Object Detection in Indoor Environment for Visually Impaired People," Second International Conference on Intelligent Computing and Control Systems, IEEE, pp. 1-4, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[61] Wafa M. Elmannai, and Khaled M. Elleithy, "A Highly Accurate and Reliable Data Fusion Framework for Guiding the Visually Impaired," IEEE Access, vol. 6, pp. 33029-33054, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[62] Sejal Gianani et al., "Juvo-An Aid for the Visually Impaired," International Conference on Smart City and Emerging Technology, IEEE, pp. 1-4, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[63] Bin Jiang et al., "Wearable Vision Assistance System based on Binocular Sensors for Visually Impaired Users," IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1375-1383, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[64] Ambrose A. Azeta, İtorobong A. Inam, and Olawande Daramola, "A Voice-Based E-Examination Framework for Visually Impaired Students in Open and Distance Learning," Turkish Online Journal of Distance Education, vol. 19, no. 2, pp. 34-46, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[65] Md. Milon Islam et al., "Developing Walking Assistants for Visually Impaired People: A Review," IEEE Sensors Journal, vol. 19, no. 8, pp. 2814-2828, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[66] Jinhui Zhu et al., "An Edge Computing Platform of Guide-Dog Robot for Visually Impaired," IEEE 14th International Symposium on Autonomous Decentralized System, pp. 1-7, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[67] Wasiq Khan et al., "Novel Framework for Outdoor Mobility Assistance and Auditory Display for Visually Impaired People," 12th International Conference on Developments in eSystems Engineering, IEEE, pp. 984-989, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[68] Jinhui Zhu et al., "A Fog Computing Model for Implementing Motion Guide to Visually Impaired," Simulation Modeling Practice and Theory, vol. 101, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[69] Dhruv Dahiya, Hardik Gupta, and Malay Kishore Dutta, "A Deep Learning based Real Time Assistive Framework for Visually Impaired," International Conference on Contemporary Computing and Applications, IEEE, pp. 106-109, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[70] Leo Abraham et al., "VISION-Wearable Speech-Based Feedback System for the Visually Impaired using Computer Vision," 4th International Conference on Trends in Electronics and Informatics, IEEE, pp. 972-976, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[71] Simerneet Singh, Nishtha Jatana, and Vasu Goel, "HELF (Haptic Encoded Language Framework): A Digital Script for Deaf-Blind and Visually Impaired," Universal Access in the Information Society, vol. 22, pp. 121-131, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[72] Uddipan Das, Vinod Namboodiri, and Hongsheng He, "Path Lookup: A Deep Learning-Based Framework to Assist Visually Impaired in Outdoor Wayfinding," IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), pp. 111-116, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[73] Leela Pravallika Siriboyina, and Venkata Sainath Gupta Thadikemalla, "A Hybrid System to Assist Visually Impaired People," SN Computer Science, vol. 2, no. 4, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[74] Saurav Kumar et al., "YOLOv4 Algorithm for the Real-Time Detection of Fire and Personal Protective Equipments at Construction Sites," Multimedia Tools and Applications, vol. 81, no. 16, pp. 22163-22183, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[75] Yu-Yun Tseng, Alexander Bell, and Danna Gurari, "VizWiz-FewShot: Locating Objects in Images Taken by People with Visual Impairments," European Conference on Computer Vision, pp. 575-591, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[76] R. Wang, C. Jung, and Y. Kim, "Seeing through Sounds: Mapping Auditory Dimensions to Data and Charts for People with Visual Impairments," Computer Graphics Forum, vol. 41, no. 3, pp.71-83, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[77] Kashif Iqbal, Zahid Majeed, and Samina Ashraf, "Exploring the Challenges of Digital Literacy among Students with Visual Impairment Studying at Higher Education Level," Pakistan Journal of Humanities and Social Sciences, vol. 10, no. 1, pp. 199-208, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[78] Harsh Dokania, and Nilanjan Chattaraj, "An Assistive Interface Protocol for Communication between Visually and Hearing-Speech Impaired Persons in Internet Platform," Disability and Rehabilitation: Assistive Technology, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[79] Mostafa Elgendy, Cecilia Sik-Lanyi, and Arpad Kelemen, "A Novel Marker Detection System for People with Visual Impairment using the Improved Tiny-YOLOv3 Model," Computer Methods and Programs in Biomedicine, vol. 205, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[80] Steindor Ellertsson, Hrafn Loftsson, and Emil L. Sigurdsson, "Artificial Intelligence in the GPs Office: A Retrospective Study on Diagnostic Accuracy," Scandinavian Journal of Primary Health Care, vol. 39, no. 4, pp. 448-458, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[81] V. Deepa, C. Sathish Kumar, and Thomas Cherian, "Automated Detection of Diabetic Retinopathy Images using Pre-Trained Convolutional Neural Network," International Conference on Communication, Control and Information Sciences, IEEE, vol. 1, pp. 1-5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]