Conjunctivitis Detection: A Comprehensive Review of Deep Learning Approaches

Background: Conjunctivitis, often referred to as “pink eye” is a highly prevalent inflammation of the conjunctiva, a thin, transparent membrane lining the white part of your eye and the inner surface of your eyelids. This inflammation triggers a cascade of symptoms that can range from mild annoyance to significant discomfort, affecting people of all ages worldwide. The three main types of conjunctivitis are viral, bacterial, and allergy. Some common symptoms include redness of the white part of the eye, ranging from watery to thick and pus-like, discharge from the eye, itching or burning sensation, and a Gritty feeling in the eye. Methods: Several deep-learning techniques for conjunctivitis detection have been developed due to their simplicity of use and their affordability. This systematic review delves into the burgeoning field of machine learning within healthcare, specifically seeking viable approaches for detecting conjunctivitis. We embark on a comparative analysis of the most successful ML algorithms currently in use regarding machine learning, including evaluation metrics, image augmentation, and the origin and size of the dataset used. Results: The results of this study provide compelling evidence for the feasibility and potential benefits of using DL algorithms for conjunctivitis detection. Conclusion: This review sheds light on the potential of machine learning in detecting Conjunctivitis, providing scientific evidence for its feasibility. By analyzing images, diagnoses, and clinical data within the medical field, the review explores how machine and deep learning algorithms can offer a wide-ranging approach to conjunctivitis detection.