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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: | Retinal Disease Classification in Medical Imaging using Quantum Computing Techniques |
| Authors Name: | V.Nyemeesha , Sanjana Karnala , Sanjana Kasireddy , Sai Varsha Kosnam , Himapriya Pothuganti |
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IJRTI_205125
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| Published Paper Id: | IJRTI2507021 |
| Published In: | Volume 10 Issue 7, July-2025 |
| DOI: | |
| Abstract: | This research examines the creation of a hybrid quantum/classical deep learning model for retinal disease classification of diabetic retinopathy, glaucoma, and cataracts, as well as normal retinal images. Classical machine learning and medical imaging systems can have difficulties with a level of complexity and subtlety concerning retinal features, which can limit diagnostic accuracy and generalization. To overcome this limitation, this study uses the power of quantum machine learning through a parameterized quantum circuit embedded in classical convolutional neural network (CNN) using PennyLane. The proposed architecture consists of a four-qubit quantum circuit as a mid-level feature extraction layer in our functionality, where the system can use entanglement and superposition to process the input data in higher-dimensional Hilbert space. The quantum layer and classical comparisons were end-to-end trained with a dataset of approximately 5000 retinal images formatted in an ImageFolder-like structure. The complete architecture was trained and tested in PyTorch on a GPU readied Google Colab environment for processing efficiencies and scalability. By merging the pattern recognition abilities of CNNs with the expressive power of quantum circuits, this hybrid approach can leverage improved classification accuracy and as well as improved feature extraction from high-dimensional image data. The findings of this research should demonstrate the feasibility and benefits of quantum-enhanced learning models applied to medical imaging, paving a new pathway towards accurate and scalable diagnostics for ophthalmology and beyond. |
| Keywords: | Quantum Computing, Medical Image Classification, Retinal Disease Diagnosis, Quantum Classifier, Quantum Machine Learning. |
| Cite Article: | "Retinal Disease Classification in Medical Imaging using Quantum Computing Techniques ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a174-a184, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507021.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: IJRTI2507021
Registration ID:205125
Published In: Volume 10 Issue 7, July-2025
DOI (Digital Object Identifier):
Page No: a174-a184 Country: Hyderabad, Telangana, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2507021 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2507021 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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