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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Automatic detection and classification of Diabetic eye disorders |
| Authors Name: | Deeksha.B , Mrs.Nita.Meshram , Charishma.C , Jyothsna.B , Bhoomika.T |
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IJNRD_211939
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| Published Paper Id: | IJNRD2401039 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | Diabetic eye diseases, particularly diabetic retinopathy, represent a critical health concern globally, demanding early detection and intervention to mitigate vision impairment. This study introduces an advanced framework harnessing the impact of machine learning (ML) and deep learning (DL) techniques for the automatically finding of diabetic eye diseases from retinal images. The methodology involves initial preprocessing steps to enhance better images and extract salient features crucial for disease identification. Subsequently, a hybrid model, integrating neural network system and ML algorithms, is trained using a diverse dataset comprising annotated retinal images. This model excels in discerning subtle and intricate patterns indicative of diabetic eye diseases. Moreover, a classification module, amalgamating DL-based feature extraction and ML-based classifiers, categorizes identified abnormalities into distinct stages of diabetic eye issues and other associated conditions. The system's architecture facilitates precise disease staging and severity assessment. The efficacy of ML-DL framework is rigorously evaluated using extensive testing datasets, showcasing remarkable accuracy, sensitivity, and specificity in detecting diverse diabetic eye diseases. Comparative analyses against established clinical standards demonstrate the system's potential to complement or surpass human expertise in disease diagnosis. This innovative fusion of ML and DL methodologies presents a robust and efficient automated systemfor diabetic eye disease detection. The framework holds significant promise in expediting early screenings, enabling timely interventions, and revolutionizing the management of diabetic eye disorders and related ocular complications.In summary, this research introduces a cutting-edge diagnostic solution that leverages ML and DL techniques, promising a transformative impact on diabetic eye disease diagnostics. Its potential to enhance patient care and facilitate proactive disease management underscores its pivotal role in addressing the challenges posed by diabetic eye diseases worldwide. |
| Keywords: | Automatic detection,retinal images, diagnostic solutions,cnn, screening |
| Cite Article: | "Automatic detection and classification of Diabetic eye disorders ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 1, page no.a342-a345, January-2024, Available :http://www.ijnrd.org/papers/IJNRD2401039.pdf |
| Downloads: | 000118763 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2401039 Registration ID: 211939 Published In: Volume 9 Issue 1, January-2024 DOI (Digital Object Identifier): Page No: a342-a345 Country: Bangalore , Karnataka , India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2401039 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2401039 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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