Red Lesion Detection in Digital Fund us Image Based on MIFNET method
DOI:
https://doi.org/10.63252/JCBECA/2.1.2025Keywords:
KNN, Diabetic Retinopathy, MIFNETAbstract
Diabetic Retinopathy (DR), a devastating eye disorder caused by diabetes mellitus, is the leading cause of blindness
in developed countries. This research explains how retinal fundus pictures may be utilised to diagnose diabetes.
Image processing and deep learning are being used to treat retinopathy. To enhance retinal fundus images, a realistic
method that employs the Hue Saturation Value (HSV), V transform technique, and histogram equalisation
approaches was used. Trials have been conducted for each phase of the image processing process and followed by
MIFNET method. Following image processing, classification analysis was performed. Twenty trials were undertaken
for each step, and average values were determined. In this experiment, the recall rate was 93.33%, the accuracy rate
was 97%, the sensitivity rate was 96.67%, the specificity rate was 93.33%, and the precision rate was 97.78%.
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