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Exp Clin Endocrinol Diabetes 2016; 124(09): 548-556
DOI: 10.1055/s-0042-108187
Article
© Georg Thieme Verlag KG Stuttgart · New York

Diagnosis of Diabetes Mellitus by Extraction of Morphological Features of Red Blood Cells Using an Artificial Neural Network

Vinupritha Palanisamy
1   SRM, Biomedical Engineering, Chennai, India
,
Anburajan Mariamichael
1   SRM, Biomedical Engineering, Chennai, India
› Author Affiliations
Further Information

Publication History

received 07 November 2015
first decision 06 May 2016

accepted 09 May 2016

Publication Date:
29 June 2016 (online)

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Abstract

Background and Aim: Diabetes mellitus is a metabolic disorder characterized by varying hyperglycemias either due to insufficient secretion of insulin by the pancreas or improper utilization of glucose. The study was aimed to investigate the association of morphological features of erythrocytes among normal and diabetic subjects and its gender-based changes and thereby to develop a computer aided tool to diagnose diabetes using features extracted from RBC.

Materials and Methods: The study involved 138 normal and 144 diabetic subjects. The blood was drawn from the subjects and the blood smear prepared was digitized using Zeiss fluorescent microscope. The digitized images were pre-processed and texture segmentation was performed to extract the various morphological features. The Pearson correlation test was performed and subsequently, classification of subjects as normal and diabetes was carried out by a neural network classifier based on the features that demonstrated significance at the level of P<0.05.

Result: The proposed system demonstrated an overall accuracy, sensitivity, specificity, positive predictive value and negative predictive value of 93.3, 93.71, 92.8, 93.1 and 93.5% respectively.

Conclusion: The morphological features exhibited a statistically significant difference (P<0.01) between the normal and diabetic cells, suggesting that it could be helpful in the diagnosis of Diabetes mellitus using a computer aided system.

Key words

segmentation - hyperglycemias - microscope - classifier - texture - computer aided system
 

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