Klasifikasi Penyakit Kronis Melalui Mata Menggunakan Algoritma Convolutional Neural Network Dengan Model MobileNet-V3 - Mohammad Haydir Awaludin Waskito; Andreas Nugroho Sihananto; Achmad Junaidi (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2024)
Chronic diseases in humans are very difficult to detect visually, for example glaucoma, hypertension, diabetes, and others. So it takes a lot of time for further medical examination by visiting a health center or hospital. Therefore, this research aims to find a solution combining medical and computer science to classify quickly and precisely. Classifying eye images requires good features and characteristics so that disease images can be classified. This research uses the Deep Learning method, namely Convolutional Neural Network with MobileNet-V3 architecture which can extract features from large resolution images very well. This research resulted in accurate classification of images of chronic diseases Normal, Diabetes, Glucoma, Cataract, Age related macular degeneration, Hypertension, Pathalogical Myopia. uses the MobileNet-V3 architecture, with transfer learning reaching 81%, and loss only 0.4913.