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Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis01:25

Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis

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Type 2 diabetes mellitus develops gradually and is often asymptomatic in early stages.Clinical ManifestationsWhen symptoms appear, they include fatigue, blurred vision, pruritus, delayed wound healing, and recurrent infections, particularly candidal infections. Peripheral neuropathy may present as numbness or tingling in the extremities. Classic hyperglycemia symptoms—polyuria, polydipsia, and polyphagia—are less common. Most patients are overweight and frequently have associated...
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Diabetic Retinopathy01:27

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DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...
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Diabetic Nephropathy01:28

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Definition Diabetic nephropathy is a chronic kidney complication that results from prolonged hyperglycemia.Prevalence It is the most common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide, affecting up to half of individuals with diabetes.Pathophysiology • Sustained hyperglycemia triggers multiple hemodynamic and metabolic changes in the kidney. • Early in the disease, increased renal blood flow and glomerular hyperfiltration...
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Diabetic Neuropathy01:22

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DefinitionDiabetic neuropathy is nerve damage caused by long-standing diabetes mellitus. It results directly from prolonged high blood sugar levels.PathophysiologyThe pathophysiology of diabetic neuropathy involves both metabolic and vascular disturbances triggered by chronic hyperglycemia.Metabolic injury: Elevated glucose levels activate the polyol pathway within nerve cells, leading to the accumulation of sorbitol and fructose. This increases oxidative stress, disrupts normal nerve...
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Related Experiment Video

Updated: May 6, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Diabetic retinopathy detection and grading system using deep learning approach.

D Siva Sundhara Raja1, S Kumarganesh2, K Martin Sagayam3

  • 1Department of ECE, SACS MAVMM Engineering College, Madurai, India.

Digital Health
|January 12, 2026
PubMed
Summary
This summary is machine-generated.

Early screening for diabetic retinopathy (DR) using automated segmentation of retinal images aids in preventing vision loss. This study presents a novel computer-assisted method for DR classification by analyzing exudates in the macular region.

Keywords:
Diabetic retinopathyexudatesmacula regionvision loss

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients.
  • Early detection and screening are crucial for preventing irreversible vision impairment.
  • Computer-assisted diagnosis offers a promising approach for efficient DR screening.

Purpose of the Study:

  • To develop and evaluate a computer-assisted system for the segmentation and classification of diabetic retinopathy.
  • To accurately segment the macular region and exudates in retinal images for DR analysis.
  • To classify DR severity (mild, moderate, severe) based on the presence and extent of exudates.

Main Methods:

  • Employed bit-plane morphological slicing for macular region segmentation.
  • Utilized U-Net deep learning for exudate segmentation in retinal images.
  • Validated methodologies on the MESSIDOR and HRF datasets, with further testing on DIARETDB1, RFMD, and FIR datasets.

Main Results:

  • Achieved high segmentation accuracy for the macular region (e.g., 99.8% AccI on HRF).
  • Demonstrated excellent exudate segmentation performance (e.g., 99.2% AccI on MESSIDOR).
  • The proposed method offers improved segmentation accuracy by locating both internal and external boundaries.

Conclusions:

  • The developed method provides accurate and efficient segmentation of macular regions and exudates.
  • The computer-assisted approach enables DR classification with high precision.
  • This technique shows potential for improving early DR screening and diagnosis, reducing vision loss.