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ARXIV:2605.11430 · MEDICAL AI · SUBMITTED 13 MAY · 20:57 UTC · FRESHNESS STALE
ARXIV:2605.11430MEDICAL AISUBMITTED 13 MAY · 20:57 UTCFRESHNESS STALENishi Doshi · Urvi Oza · Pankaj Kumar · arXiv
A novel Multi Channel Inception V3 architecture with custom preprocessing and downscaling algorithms for improved Diabetic Retinopathy classification.
Opportunity summary
Pain A novel Multi Channel Inception V3 architecture with custom preprocessing and downscaling algorithms for improved Diabetic Retinopathy classification.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A novel Multi Channel Inception V3 architecture with custom preprocessing and downscaling algorithms for improved Diabetic Retinopathy classification. DR classification deals with classifying retinal fundus image into five stages on the basis of severity…
Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with classifying retinal fundus image into five stages on the basis of…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We report results of proposed approach using accuracy, specificity and sensitivity, which outperform the previous state of the art methods. Code availability is flagged…
Medical AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A novel Multi Channel Inception V3 architecture with custom preprocessing and downscaling algorithms for improved Diabetic Retinopathy classification.
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10.48550/arXiv.2605.11430A novel Multi Channel Inception V3 architecture with custom preprocessing and downscaling algorithms for improved Diabetic Retinopathy classification.
Abstract
Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with classifying retinal fundus image into five stages on the basis of severity of diabetes. One of the major issue faced while dealing with DR classification problem is the large and varying size of images. In this paper we propose and explore the use of several downscaling algorithms before feeding the image data to a Deep Learning Network for classification. For improving training and testing; we amalgamate two datasets: Kaggle and Indian Diabetic Retinopathy Image Dataset. Our experiments have been performed on a novel Multi Channel Inception V3 architecture with a unique self crafted preprocessing phase. We report results of proposed approach using accuracy, specificity and sensitivity, which outperform the previous state of the art methods. Index Terms: Diabetic Retinopathy, Downscaling Algorithms, Multichannel CNN Architecture, Deep Learning
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Dimensions overall score 7.0
PROBLEM
A novel Multi Channel Inception V3 architecture with custom preprocessing and downscaling algorithms for improved Diabetic Retinopathy classification. DR classification deals with classifying retinal fundus image into five stages on the basis of severity of diabetes.
METHOD
Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with classifying retinal fundus image into five stages on the basis of severity of diabetes.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We report results of proposed approach using accuracy, specificity and sensitivity, which outperform the previous state of the art methods. Code availability is flagged in the production record; the publi...
WHY NOW
Medical AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A novel Multi Channel Inception V3 architecture with custom preprocessing and downscaling algorithms for improved Diabetic Retinopathy classification. DR classification deals with classifying retinal fundus image into five stages on the basis of severity of diabetes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with classifying retinal fundus image into five stages on the basis of severity of diabetes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We report results of proposed approach using accuracy, specificity and sensitivity, which outperform the previous state of the art methods. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel Multi Channel Inception V3 architecture with custom preprocessing and downscaling algorithms for improved Diabetic Retinopathy classification.
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Medical AI
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Commercial read
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