An Intelligent Diabetic Retinopathy Forecasting System using Modified Deep Neural Network Article Swipe
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.52783/jns.v14.1566
Diabetic Retinopathy (DR) is a severe complication of diabetes that can lead to vision impairment and blindness if not detected and treated early. In this research, we propose an advanced forecasting system leveraging a Modified Deep Neural Network (DNN) to enhance the accuracy and efficiency of DR diagnosis. The proposed system integrates a deep learning framework with modifications tailored to the unique characteristics of retinal images affected by diabetes. We introduce specialized features extraction techniques and optimize the network architecture to accommodate the intricacies of diabetic retinal pathology. A comprehensive dataset comprising diverse retinal images is utilized for training and validating the modified DNN model. The experimental results demonstrate superior forecasting accuracy compared to existing methods, highlighting the effectiveness of the proposed approach in early detection and prognosis of diabetic retinopathy. This intelligent forecasting system holds significant promise for improving the clinical management of diabetic patients by facilitating timely intervention and reducing the risk of irreversible visual impairment.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.52783/jns.v14.1566
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407857680
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4407857680Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.52783/jns.v14.1566Digital Object Identifier
- Title
-
An Intelligent Diabetic Retinopathy Forecasting System using Modified Deep Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-06Full publication date if available
- Authors
-
Anamika Raj, Noor Maizura Mohamad Noor, Rosmayati Mohemad, Noor Azliza Che Mat, Shahid Hussain, Shahid HussainList of authors in order
- Landing page
-
https://doi.org/10.52783/jns.v14.1566Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.52783/jns.v14.1566Direct OA link when available
- Concepts
-
Medicine, Diabetic retinopathy, Artificial neural network, Artificial intelligence, Machine learning, Optometry, Physical medicine and rehabilitation, Computer science, Diabetes mellitus, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4407857680 |
|---|---|
| doi | https://doi.org/10.52783/jns.v14.1566 |
| ids.doi | https://doi.org/10.52783/jns.v14.1566 |
| ids.openalex | https://openalex.org/W4407857680 |
| fwci | 0.0 |
| type | article |
| title | An Intelligent Diabetic Retinopathy Forecasting System using Modified Deep Neural Network |
| biblio.issue | 1S |
| biblio.volume | 14 |
| biblio.last_page | 495 |
| biblio.first_page | 485 |
| topics[0].id | https://openalex.org/T11438 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.996399998664856 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Retinal Imaging and Analysis |
| topics[1].id | https://openalex.org/T11396 |
| topics[1].field.id | https://openalex.org/fields/36 |
| topics[1].field.display_name | Health Professions |
| topics[1].score | 0.9442999958992004 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3605 |
| topics[1].subfield.display_name | Health Information Management |
| topics[1].display_name | Artificial Intelligence in Healthcare |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C71924100 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7318769693374634 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[0].display_name | Medicine |
| concepts[1].id | https://openalex.org/C2779829184 |
| concepts[1].level | 3 |
| concepts[1].score | 0.6706472635269165 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q631361 |
| concepts[1].display_name | Diabetic retinopathy |
| concepts[2].id | https://openalex.org/C50644808 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6141142249107361 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[2].display_name | Artificial neural network |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5355128645896912 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C119857082 |
| concepts[4].level | 1 |
| concepts[4].score | 0.372392863035202 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[4].display_name | Machine learning |
| concepts[5].id | https://openalex.org/C119767625 |
| concepts[5].level | 1 |
| concepts[5].score | 0.35906606912612915 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q618211 |
| concepts[5].display_name | Optometry |
| concepts[6].id | https://openalex.org/C99508421 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3319683372974396 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2678675 |
| concepts[6].display_name | Physical medicine and rehabilitation |
| concepts[7].id | https://openalex.org/C41008148 |
| concepts[7].level | 0 |
| concepts[7].score | 0.2559016942977905 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[7].display_name | Computer science |
| concepts[8].id | https://openalex.org/C555293320 |
| concepts[8].level | 2 |
| concepts[8].score | 0.20606058835983276 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q12206 |
| concepts[8].display_name | Diabetes mellitus |
| concepts[9].id | https://openalex.org/C134018914 |
| concepts[9].level | 1 |
| concepts[9].score | 0.06814569234848022 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q162606 |
| concepts[9].display_name | Endocrinology |
| keywords[0].id | https://openalex.org/keywords/medicine |
| keywords[0].score | 0.7318769693374634 |
| keywords[0].display_name | Medicine |
| keywords[1].id | https://openalex.org/keywords/diabetic-retinopathy |
| keywords[1].score | 0.6706472635269165 |
| keywords[1].display_name | Diabetic retinopathy |
| keywords[2].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[2].score | 0.6141142249107361 |
| keywords[2].display_name | Artificial neural network |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5355128645896912 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/machine-learning |
| keywords[4].score | 0.372392863035202 |
| keywords[4].display_name | Machine learning |
| keywords[5].id | https://openalex.org/keywords/optometry |
| keywords[5].score | 0.35906606912612915 |
| keywords[5].display_name | Optometry |
| keywords[6].id | https://openalex.org/keywords/physical-medicine-and-rehabilitation |
| keywords[6].score | 0.3319683372974396 |
| keywords[6].display_name | Physical medicine and rehabilitation |
| keywords[7].id | https://openalex.org/keywords/computer-science |
| keywords[7].score | 0.2559016942977905 |
| keywords[7].display_name | Computer science |
| keywords[8].id | https://openalex.org/keywords/diabetes-mellitus |
| keywords[8].score | 0.20606058835983276 |
| keywords[8].display_name | Diabetes mellitus |
| keywords[9].id | https://openalex.org/keywords/endocrinology |
| keywords[9].score | 0.06814569234848022 |
| keywords[9].display_name | Endocrinology |
| language | en |
| locations[0].id | doi:10.52783/jns.v14.1566 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764549079 |
| locations[0].source.issn | 2226-0439 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2226-0439 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of Neonatal Surgery |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Journal of Neonatal Surgery |
| locations[0].landing_page_url | https://doi.org/10.52783/jns.v14.1566 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5035756052 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Anamika Raj |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Anamika Raj |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5019686916 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-9960-571X |
| authorships[1].author.display_name | Noor Maizura Mohamad Noor |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Noor Maizura Mohamad Noor |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5018720819 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5573-4293 |
| authorships[2].author.display_name | Rosmayati Mohemad |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Rosmayati Mohemad |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5003729613 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-8335-6552 |
| authorships[3].author.display_name | Noor Azliza Che Mat |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Noor Azliza Che Mat |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5053718523 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-4826-3339 |
| authorships[4].author.display_name | Shahid Hussain |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Shahid Hussain |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5053718523 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-4826-3339 |
| authorships[5].author.display_name | Shahid Hussain |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Shahid Hussain |
| authorships[5].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.52783/jns.v14.1566 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | An Intelligent Diabetic Retinopathy Forecasting System using Modified Deep Neural Network |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11438 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.996399998664856 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Retinal Imaging and Analysis |
| related_works | https://openalex.org/W3203337527, https://openalex.org/W2044207570, https://openalex.org/W1980571360, https://openalex.org/W4244634705, https://openalex.org/W3023629149, https://openalex.org/W2912406428, https://openalex.org/W2100530570, https://openalex.org/W4256153729, https://openalex.org/W4315497909, https://openalex.org/W4395042183 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.52783/jns.v14.1566 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764549079 |
| best_oa_location.source.issn | 2226-0439 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2226-0439 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Journal of Neonatal Surgery |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Journal of Neonatal Surgery |
| best_oa_location.landing_page_url | https://doi.org/10.52783/jns.v14.1566 |
| primary_location.id | doi:10.52783/jns.v14.1566 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764549079 |
| primary_location.source.issn | 2226-0439 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2226-0439 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of Neonatal Surgery |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Neonatal Surgery |
| primary_location.landing_page_url | https://doi.org/10.52783/jns.v14.1566 |
| publication_date | 2025-02-06 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 88 |
| abstract_inverted_index.a | 4, 33, 52 |
| abstract_inverted_index.DR | 46 |
| abstract_inverted_index.In | 23 |
| abstract_inverted_index.We | 69 |
| abstract_inverted_index.an | 28 |
| abstract_inverted_index.by | 67, 146 |
| abstract_inverted_index.if | 17 |
| abstract_inverted_index.in | 123 |
| abstract_inverted_index.is | 3, 95 |
| abstract_inverted_index.of | 7, 45, 63, 84, 119, 128, 143, 154 |
| abstract_inverted_index.to | 12, 39, 59, 80, 113 |
| abstract_inverted_index.we | 26 |
| abstract_inverted_index.DNN | 103 |
| abstract_inverted_index.The | 48, 105 |
| abstract_inverted_index.and | 15, 20, 43, 75, 99, 126, 150 |
| abstract_inverted_index.can | 10 |
| abstract_inverted_index.for | 97, 138 |
| abstract_inverted_index.not | 18 |
| abstract_inverted_index.the | 41, 60, 77, 82, 101, 117, 120, 140, 152 |
| abstract_inverted_index.(DR) | 2 |
| abstract_inverted_index.Deep | 35 |
| abstract_inverted_index.This | 131 |
| abstract_inverted_index.deep | 53 |
| abstract_inverted_index.lead | 11 |
| abstract_inverted_index.risk | 153 |
| abstract_inverted_index.that | 9 |
| abstract_inverted_index.this | 24 |
| abstract_inverted_index.with | 56 |
| abstract_inverted_index.(DNN) | 38 |
| abstract_inverted_index.early | 124 |
| abstract_inverted_index.holds | 135 |
| abstract_inverted_index.Neural | 36 |
| abstract_inverted_index.early. | 22 |
| abstract_inverted_index.images | 65, 94 |
| abstract_inverted_index.model. | 104 |
| abstract_inverted_index.severe | 5 |
| abstract_inverted_index.system | 31, 50, 134 |
| abstract_inverted_index.timely | 148 |
| abstract_inverted_index.unique | 61 |
| abstract_inverted_index.vision | 13 |
| abstract_inverted_index.visual | 156 |
| abstract_inverted_index.Network | 37 |
| abstract_inverted_index.dataset | 90 |
| abstract_inverted_index.diverse | 92 |
| abstract_inverted_index.enhance | 40 |
| abstract_inverted_index.network | 78 |
| abstract_inverted_index.promise | 137 |
| abstract_inverted_index.propose | 27 |
| abstract_inverted_index.results | 107 |
| abstract_inverted_index.retinal | 64, 86, 93 |
| abstract_inverted_index.treated | 21 |
| abstract_inverted_index.Diabetic | 0 |
| abstract_inverted_index.Modified | 34 |
| abstract_inverted_index.accuracy | 42, 111 |
| abstract_inverted_index.advanced | 29 |
| abstract_inverted_index.affected | 66 |
| abstract_inverted_index.approach | 122 |
| abstract_inverted_index.clinical | 141 |
| abstract_inverted_index.compared | 112 |
| abstract_inverted_index.detected | 19 |
| abstract_inverted_index.diabetes | 8 |
| abstract_inverted_index.diabetic | 85, 129, 144 |
| abstract_inverted_index.existing | 114 |
| abstract_inverted_index.features | 72 |
| abstract_inverted_index.learning | 54 |
| abstract_inverted_index.methods, | 115 |
| abstract_inverted_index.modified | 102 |
| abstract_inverted_index.optimize | 76 |
| abstract_inverted_index.patients | 145 |
| abstract_inverted_index.proposed | 49, 121 |
| abstract_inverted_index.reducing | 151 |
| abstract_inverted_index.superior | 109 |
| abstract_inverted_index.tailored | 58 |
| abstract_inverted_index.training | 98 |
| abstract_inverted_index.utilized | 96 |
| abstract_inverted_index.blindness | 16 |
| abstract_inverted_index.detection | 125 |
| abstract_inverted_index.diabetes. | 68 |
| abstract_inverted_index.framework | 55 |
| abstract_inverted_index.improving | 139 |
| abstract_inverted_index.introduce | 70 |
| abstract_inverted_index.prognosis | 127 |
| abstract_inverted_index.research, | 25 |
| abstract_inverted_index.comprising | 91 |
| abstract_inverted_index.diagnosis. | 47 |
| abstract_inverted_index.efficiency | 44 |
| abstract_inverted_index.extraction | 73 |
| abstract_inverted_index.impairment | 14 |
| abstract_inverted_index.integrates | 51 |
| abstract_inverted_index.leveraging | 32 |
| abstract_inverted_index.management | 142 |
| abstract_inverted_index.pathology. | 87 |
| abstract_inverted_index.techniques | 74 |
| abstract_inverted_index.validating | 100 |
| abstract_inverted_index.Retinopathy | 1 |
| abstract_inverted_index.accommodate | 81 |
| abstract_inverted_index.demonstrate | 108 |
| abstract_inverted_index.forecasting | 30, 110, 133 |
| abstract_inverted_index.impairment. | 157 |
| abstract_inverted_index.intelligent | 132 |
| abstract_inverted_index.intricacies | 83 |
| abstract_inverted_index.significant | 136 |
| abstract_inverted_index.specialized | 71 |
| abstract_inverted_index.architecture | 79 |
| abstract_inverted_index.complication | 6 |
| abstract_inverted_index.experimental | 106 |
| abstract_inverted_index.facilitating | 147 |
| abstract_inverted_index.highlighting | 116 |
| abstract_inverted_index.intervention | 149 |
| abstract_inverted_index.irreversible | 155 |
| abstract_inverted_index.retinopathy. | 130 |
| abstract_inverted_index.comprehensive | 89 |
| abstract_inverted_index.effectiveness | 118 |
| abstract_inverted_index.modifications | 57 |
| abstract_inverted_index.characteristics | 62 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.075224 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |