Hsu, Heng ChiHeng ChiHsuLin, Cheng HungCheng HungLinLu, Cheng KaiCheng KaiLuJIA-KANG WANGHuang, Tzu LunTzu LunHuang2024-01-202024-01-202022-01-0197816654415440747668Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/638667To popularize age-related macular degeneration diagnosis in rural and remote areas, we proposed a lightweight convolution neural network (CNN) architecture that aims to identify whether the patient has age-related macular degeneration through the optical coherence tomography images. The proposed CNN model achieves 2, 322 parameters and 0.0573 GFLOPs, which is only 4.98% of the Mobile net. Besides, the CNN model achieves 98.18% of accuracy. With the fixed-point simulation using 14 bits on weights and 5 bits on input data, the CNN model achieves accuracy of 97.73 %.Age-related macular degeneration | convolution neural network | deep learning | optical coherence tomography[SDGs]SDG11conference paper10.1109/ICCE53296.2022.97305622-s2.0-85127044864https://api.elsevier.com/content/abstract/scopus_id/85127044864