Abstract: Myopia commonly known as near sightedness, is a prevalent vision problem affecting a considerable portion of the global population, particularly among adolescents and young adults. Detecting myopia early is crucial to effectively manage and prevent associated complications such as retinal detachment, myopic macular degeneration, and glaucoma. While traditional methods of myopia detection often rely on subjective evaluations by eye care professionals, which can be time-consuming and require specialized equipment, our study proposes a novel approach using deep learning techniques. By harnessing advancements in computer vision and deep learning, we have developed a convolutional neural network (CNN) model trained on a large dataset of retinal images. This model is capable of automatically identifying signs of myopia, including optic disc anomalies, retinal stretching, and other characteristic features associated with myopic progression. Our experimental findings demonstrate the effectiveness of this deep learning model in accurately detecting myopia from retinal images with high sensitivity and specificity. Furthermore, the model's performance surpasses that of traditional methods, offering a more efficient and objective approach to myopia detection. This system we have developed shows promise for early screening initiatives, telemedicine applications, and assisting healthcare professionals in the timely diagnosis and management of myopia-related conditions.
Keywords: Myopia detection, Deep learning, Convolutional neural networks, Retinal imaging, Healthcare AI.
Cite:
Anusha, Anusha Sadashiva Lokeshwar, Arpita Sanyal, Deekshitha, Rejeesh Rayaroth "Detection of Pathological Myopia using Deep Learning Techniques", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13393.
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DOI:
10.17148/IJARCCE.2024.13393
