Open AccessPredicting Retinal Diseases using Efficient Image Processing and Convolutional Neural Network (CNN)
Author(s) -
Muhammad Asif,
Mahruf Zaman Utso,
Shifat Bin Habib,
Amit Kumar Das
Publication year - 2021
Publication title -
journal of engineering advancements
Language(s) - English
Resource type - Journals
eISSN - 2708-6437
pISSN - 2708-6429
DOI - 10.38032/jea.2021.04.008
Subject(s) - convolutional neural network , computer science , deep learning , artificial intelligence , drusen , preprocessor , image processing , artificial neural network , retinal , pattern recognition (psychology) , image (mathematics) , computer vision , ophthalmology , medicine
Neural networks in image processing are becoming a more crucial and integral part of machine learning as computational technology and hardware systems are advanced. Deep learning is also getting attention from the medical sector as it is a prominent process for classifying diseases. There is a lot of research to predict retinal diseases using deep learning algorithms like Convolutional Neural Network (CNN). Still, there are not many researches for predicting diseases like CNV which stands for choroidal neovascularization, DME, which stands for Diabetic Macular Edema; and DRUSEN. In our research paper, the CNN (Convolutional Neural Networks) algorithm labeled the dataset of OCT retinal images into four types: CNV, DME, DRUSEN, and Natural Retina. We have also done several preprocessing on the images before passing these to the neural network. We have implemented different models for our algorithm where individual models have different hidden layers. At the end of our following research, we have found that our algorithm CNN generates 93% accuracy.