Applying Deep Learning and Computer Vision for Early Diagnosis of Eye Diseases

Medical image processing has a significant role in clinical investigation and recent medical research. An appropriate image-based medical assessment helps to analyze or detect critical diseases early, as it has a high value of medical information. In this study, medical imaging is reviewed for the diagnosis of eye diseases using computational intelligence. However, the identification of these diseases using traditional image processing is quite complicated. Nowadays, various machine learning and deep learning approaches are developed for the detection of different eye diseases which are helpful for the detection of the diseases at an early stage. Research showed that eye disorders are more serious in emerging or underdeveloped nations due to inadequate healthcare facilities and skilled health workers. An estimate of 45 million people around the world are blind and the tragic fact is that only 75% of these cases are curable. Moreover, the doctor-patient ratio around the globe is about 1: 10,000. Therefore, it takes an hour to create a screening system for the identification of these illnesses. Ophthalmology is close to making breakthroughs in evaluating, diagnosing, and treating eye diseases. Additionally, many eye and vision problems show no obvious signs. As a consequence, people are often unaware that problems exist. Early detection of diseases is a primary concern as they could be easily cured before leading to severity. This research paper focuses on detecting eye illnesses, such as Diabetic retinopathy, Diabetic Macular Edema, Glaucoma, Age macular Degeneration, Retinal Vascular Occlusions, and Retinal Detachment. The authors explore various algorithms, imaging modalities, and challenges in this context. The study aims to raise awareness about eye disorders leading to blindness using computer vision, image processing, and deep learning techniques. It also investigates how these machine learning and deep learning approaches can aid in early disease diagnoses for effective treatment before vision loss occurs.

Keywords: Age-related macular degeneration, Cataract, Deep learning, Diabetic retinopathy, Glaucoma, Heidelberg retinal tomography, Optical coherence tomography, Ultrasound imaging.