Early Detection and Treatment of Glaucoma and Diabetic Retinopathy Using Deep Neural Networks and Fuzzy Logic

Abstract

Early detection, Monitoring and treatment of eye diseases like glaucoma and diabetic retinopathy (DR) has considerable impact in preventing avoidable blindness and improving the patient outcome. Recently, a lot of attention has been paid for utilizing some modern computational techniques such as Deep Neural Networks (DNNs) and Fuzzy Logic in the field of medical diagnostics because they provide an efficient way to automate the processes and improve disease detection. This paper describes our methodology for the implementation of DNNs with Fuzzy Logic to a diagnosis at early stage and then management glaucoma and DR, two most common irreversible blinding diseases worldwide. In this method, DNNs are used for interpreting complex images associated with eye pathology (fundus camera photographs and optical coherence tomography scans), and identifying early-stage pathological signals for glaucoma and DR, where the deep learning algorithms are trained on a large set of annotated retinal images to detect anomalies such as optic nerve head pointing defects, flame hemorrhages or microaneurysms separate classifiers that engage part of the wider algorithm. Fuzzy Logic integration in this structure further bolsters the units by envelope to conceptual difficulties and vagueness normally happen in medicinal imaging, remarkably because each proffers function from a border zone someplace attributes engine be foggy alternatively might contrast between patients. Fuzzy Logic Fuzzy logic could be used to improve decision-making by defining the extent of disease severity and straightforward outputs in order to allow clinicians to enhance treatment decisions. The deployment not only looks for the technical success of such a combination but also solutions for its computational burden, or answering addressing issues related to data quality and real-time processing requirements. Results in this preclinical testing session exhibit the potential of the system to not only enhance diagnostic accurateness and speed, but also hint at deployable conditions such as resource-limited clinical settings, especially in areas with low access to specific healthcare professionals. The approach will be refined with future work in order to use the algorithm on a larger dataset, and clinical trials should be performed in real-world clinical settings using more diverse patient populations. This implementation strategy holds great promise for eye care in revolutionizing early detection and timely intervention for glaucoma and DR leading to improved patient care, and lower rates of vision loss.