CATARACT CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN) INCEPTION RESNETV2 - Zulfa, M. Mauludin; Sri Kusuma Aditya, Christian (Informatika, Universitas Jenderal Soedirman, 2024)
The eye is a human sensory device that functions as an organ of vision. Referring to data from the World Health Organization (WHO) in 2018, cataracts are responsible for 48% of blindness cases in the world and are the main cause in Indonesia. People still find it difficult to distinguish cataract eyes from normal eyes, so they often do not realize the indications of cataract disease. It is important to conduct early detection of cataract disease before blindness occurs. As technology develops, cataract identification becomes easier and simpler with digital image processing classification. This research develops a cataract image classification model using Convolutional Neural Network (CNN) with Inception-ResnetV2 architecture to identify cataract eyes with normal eyes. The proposed model consists of two parts of Inception-ResnetV2 architecture as the base model, and the head model in the form of Fully Connected Layers consisting of global average polling, 2 dense relu layers of 128 and 256 neurons, 2 batch normalization layers, 2 layers of dropout parameter 0.5, and softmax activation function for the output layer. To improve model training, the Stochastic Gradient Descent (SGD) optimization function is used. The dataset consists of 2,192 eye fundus images with 2 main classes of cataract and normal taken from the public data provider site Kaggle. Learning rate tests on the optimization function were carried out with parameters 0.1, 0.01, and 0.001, the results of the proposed model compiled with Stochastic Gradient Descent (SGD) learning rate 0.01 gave a final accuracy of 96%.