Sequential Model Using Explainable AI Method to Detect Eye Diseases

Eye diseases refer to a wide range of conditions that affect the structure or function of the eye, causing vision impairment or blindness. Some of the eye diseases include Diabetic Retinopathy, Cataract, Glaucoma, etc. These diseases can be caused by various factors, such as age, genetics, lifestyle, or underlying health conditions. Early detection and treatment are critical to preventing vision loss and preserving eye health. Treatment for diseases such as glaucoma typically involves the use of eye drops, laser therapy, or surgery to reduce intraocular pressure, which is the main risk factor for the disease. Certain diseases can be difficult to detect in their early stages, and many patients may not realize they have the disease until they experience vision loss. This can make it more challenging to manage the disease effectively. This is where Explainable AI (XAI) comes into action. XAI is an approach in which Artificial Intelligence models are designed to provide clear and transparent reasoning for their decisions. In the context of certain eye diseases, this would mean that AI models can provide clinicians with a detailed explanation of how they arrived at a particular diagnosis or treatment recommendation. In cases of specific eye diseases, timely identification and treatment can have a significant impact on the advancement of the disease, making this approach especially valuable.