Abstract

Among the many complex sensory organs, the human eye stands out. Prevention of eye illnesses is of the utmost importance since irreversible vision loss might result from postponed treatment. Therefore, to manage the continuous course of eye illnesses, early identification and monitoring are vital. Hyperemia, or redness, from increased blood flow, is one sign of conjunctivitis, an eye disorder defined by inflammation of the conjunctiva. Many illnesses and problems are often curable or significantly reducible with the help of the greatest medicines, sophisticated procedures, and early, accurate diagnosis by medical experts. Because there is a severe lack of diagnostic specialists, patients with vision difficulties are often not given the care they need because their conditions are not properly diagnosed. Segmentation approaches are crucial for detecting and quantifying hyperemic areas, which are necessary for diagnosing and evaluating conjunctivitis. This paper presents a very efficient machine learning framework for the detection of eye problems. The system does this by integrating segmentation approaches with feature extraction techniques. The use of the discrete cosine transform (DCT) generates feature vectors from the segmented regions of interest. Random forests and neural networks are two machine learning classifiers that are learned using feature vectors. This approach has a 96% success rate and has potential to assist ophthalmologists in assessing the severity of illnesses in objective and accurate manner. This strategy might be advantageous for adopting ICT-based solutions in public health systems in remote regions.