IrisBased Disease Diagnosis Using Advanced Computer Vision

The early detection of eye diseases plays a crucial role in preventing vision loss and ensuring effective treatment. This study proposes an Iris-Based Disease Diagnosis system using advanced computer vision techniques to detect various eye conditions from iris images. The dataset used in this research, Iris Disease Detection Dataset from Kaggle, contains images classified into different disease categories, including Central Serous Chorioretinopathy, Diabetic Retinopathy, Disc Edema, Glaucoma, Healthy, Macular Scar, Myopia, Pterygium, Retinal Detachment, and Retinitis Pigmentosa. These conditions represent a wide range of eye diseases that can lead to severe vision impairments if left undiagnosed.

Keywords: Iris disease diagnosis, computer vision, deep learning, Convolutional Neural Networks (CNN), MobileNet, DenseNet, eye disease detection, retinal diseases, iris image classification, disease detection system, machine learning, automated diagnosis, early detection, healthcare, medical image analysis, image preprocessing, Kaggle datasets.