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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | A Survey on Multi-Class Retinal Disease Detection using CNN |
| Authors Name: | Kavitha B , Phalguni S Prasad , Pragna G Prasad , Prajna SG , Raghavan V |
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| Author Reg. ID: |
IJRTI_188847
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| Published Paper Id: | IJRTI2401042 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | Retinal diseases pose a significant threat to vision, necessitating early detection for effective intervention and improved patient outcomes. This survey paper explores the critical intersection of retinal disease detection and deep learning, with a specific emphasis on minimizing memory consumption. The increasing prevalence of medical imaging data, particularly retinal scans, underscores the need for efficient and accurate diagnostic tools. The paper begins by elucidating the significance of early detection in retinal diseases, emphasizing its profound impact on preserving vision, improving treatment efficacy, and preventing severe complications. The role of deep learning, notably Convolutional Neural Networks (CNNs), in automating the detection process is then introduced. CNNs exhibit a remarkable ability to handle large volumes of medical imaging data, learning hierarchical features from retinal scans without explicit feature engineering. The survey meticulously Examines current methods for creating memory-efficient Convolutional Neural Networks, encompassing techniques such as model compression, quantization, and the design of lightweight architectures. This exploration extends to studies focused on multiclass retinal disease detection, considering diverse diseases and dataset characteristics. The study explores hybrid architectures that combine deep learning and transfer learning to assess their capability in achieving a balance between accuracy and minimal memory usage. Evaluation metrics and comparative analyses are presented, shedding light on the performance of various approaches in terms of accuracy, sensitivity, specificity, and computational efficiency. The survey concludes by outlining remaining challenges in achieving efficient multiclass retinal disease detection and proposes future directions for research. |
| Keywords: | Retinal Diseases, Deep Learning, Convolutional Neural Networks, Medical Image Analysis, Early Detection, Memory Efficient Models, Multiclass Classification, Retinal Disease Diagnosis, Model Compression, Quantization, Lightweight Architectures, Transfer Learning, Comparative Analysis, Diagnostic Accuracy, Ophthalmology, Healthcare Technology, Vision Preservation, Retinal Scans, Research Challenges, Future Directions |
| Cite Article: | "A Survey on Multi-Class Retinal Disease Detection using CNN", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.242 - 248, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401042.pdf |
| Downloads: | 000205100 |
| ISSN: |
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: IJRTI2401042
Registration ID:188847
Published In: Volume 9 Issue 1, January-2024
DOI (Digital Object Identifier):
Page No: 242 - 248 Country: Bengaluru, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2401042 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2401042 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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