• Sanika Sasane
• Neha Wadkar
• Shreya Yadav
• Mayuri Mane
• Kiran Yesugade

High myopia and pathological myopia pose serious threats to visual health, necessitating accurate and early detection for effective intervention. This research focuses on leveraging Convolutional Neural Networks (CNNs) for the automated detection and classification of high myopia and pathological myopia from fundus images. CNNs have proven to be powerful tools in image analysis tasks, particularly in discerning intricate patterns and features. Fundus photographs and optical coherence tomography scans are employed to capture detailed anatomical structures associated with high myopia and pathological myopia.

Myopia, Nearsightedness, Myopic vision, Vision problems, CNN image detection.

"DEEP LEARNING TECHNIQUES FOR DETECTION OF MYOPIC DISORDERS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.d740-d745, March-2024, Available :http://www.jetir.org/papers/JETIR2403391.pdf

"DEEP LEARNING TECHNIQUES FOR DETECTION OF MYOPIC DISORDERS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppd740-d745, March-2024, Available at : http://www.jetir.org/papers/JETIR2403391.pdf