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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19455
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
Total Countries : 144
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| Paper Title: | Implementation and Evaluation on Multi-Class Retinal Disease Detection using CNN |
| Authors Name: | Phalguni S Prasad , Pragna G Prasad , Prajna SG , Raghavan V , Kavitha B |
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IJRTI_189929
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| Published Paper Id: | IJRTI2405068 |
| Published In: | Volume 9 Issue 5, May-2024 |
| DOI: | |
| Abstract: | The project delves into the crucial overlap of detecting retinal diseases and deep learning, with a specific focus on reducing memory usage. It stresses the importance of early detection in preserving vision, enhancing treatment effectiveness, and averting severe complications. Convolutional Neural Networks (CNNs) are emphasized as potent tools for automating detection, capable of managing vast amounts of medical imaging data without explicit feature engineering. Diverse techniques for developing memory-efficient CNNs are examined, including model compression, quantization, and lightweight architecture design. The study expands its analysis to encompass multiclass retinal disease detection, considering various diseases and dataset attributes. Hybrid architectures that merge deep learning and transfer learning are explored to strike a balance between accuracy and minimal memory consumption. Assessment metrics and comparative analyses are provided to shed light on the performance of different methodologies. The paper concludes by delineating remaining challenges in achieving efficient multiclass retinal disease detection. |
| Keywords: | Retinal Diseases, Deep Learning, Convolutional Neural Networks, Medical Image Analysis, Early Detection, Memory Efficient Models, Multiclass Classification, Retinal Disease Diagnosis, Model Compression,Quantization,LightweightArchitectures Diagnostic Accuracy, Ophthalmology, Healthcare Technology, Vision Preservation Future Directions. |
| Cite Article: | "Implementation and Evaluation on Multi-Class Retinal Disease Detection using CNN", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 5, page no.459 - 464, May-2024, Available :http://www.ijrti.org/papers/IJRTI2405068.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2405068
Registration ID:189929
Published In: Volume 9 Issue 5, May-2024
DOI (Digital Object Identifier):
Page No: 459 - 464 Country: Bangalore, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2405068 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2405068 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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