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Title |
A Novel Deep Learning Based Architecture for Measuring Diabetes |
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Author |
Shaima Sharaf |
| Citation |
Vol. 24 No. 9 pp. 119-126 |
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Abstract |
Diabetes is a chronic condition that happens when the pancreas fails to produce enough insulin or when the body's insulin is ineffectively used. Uncontrolled diabetes causes hyperglycaemia, or high blood sugar, which causes catastrophic damage to many of the body's systems, including the neurons and blood vessels, over time. The burden of disease on the global healthcare system is enormous. As a result, early diabetes diagnosis is critical in saving many lives. Current methods for determining whether a person has diabetes or is at risk of acquiring diabetes, on the other hand, rely heavily on clinical biomarkers. This research presents a unique deep learning architecture for predicting whether or not a person has diabetes and the severity levels of diabetes from the person’s retinal image. This study incorporates datasets such as EyePACS and IDRID, which comprise Diabetic Retinopathy (DR) images and uses Dense-121 as the base due to its improved performance. |
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Keywords |
Convolutional neural network, deep learning, diabetes, machine learning, diabetic retinopathy |
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