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.
