International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Optimizing the Hyperparameters of CNN in predicting the presence of Retinal Disease with OCT image dataset

Author : GOKILA BRINDHA P ¹ OM PRAKASH S ² YASWANTH M ³

Date of Publication :21st July 2021

Abstract: Accurate detection of diabetic macular edema (DME) is a crucial task in optical coherence tomography (OCT) images of the attention. Age-related degeneration (AMD) and diabetic macular edema disease are the leading causes of blindness being diagnosed. Fully automating OCT image detection can significantly decrease the tedious clinician labour and acquire a faithful pre- diagnosis from the analysis of the structural elements of the retina. Deep learning techniques are often applied within the prediction of the attention disease. The goal of this project is to optimize the hyperparameters of CNN model. During this work a study has been administered using grid search and random search optimization algorithms in tuning the hyperparameters of CNN model. The analysis done by evaluating the performance of those algorithms on classification of retinal disease.

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