视网膜OCT图像深度学习方法的比较研究

在医学领域，计算机科学在疾病检测和诊断中的应用正日益普及。以前，疾病的检测通常要花费大量时间，而且可靠性较低。..

Comparisonal study of Deep Learning approaches on Retinal OCT Image

In medical science, the use of computer science in disease detection and diagnosis is gaining popularity. Previously, the detection of disease used to take a significant amount of time and was less reliable.Machine learning (ML) techniques employed in recent biomedical researches are making revolutionary changes by gaining higher accuracy with more concise timing. At present, it is even possible to automatically detect diseases from the scanned images with the help of ML. In this research, we have taken such an attempt to detect retinal diseases from optical coherence tomography (OCT) X-ray images. Here, we propose a deep learning (DL) based approach in detecting retinal diseases from OCT images which can identify three conditions of the retina. Four different models used in this approach are compared with each other. On the test set, the detection accuracy is 98.00\% for a vanilla convolutional neural network (CNN) model, 99.07\% for Xception model, 97.00\% for ResNet50 model, and 99.17\% for MobileNetV2 model. The MobileNetV2 model acquires the highest accuracy, and the closest to the highest is the Xception model. The proposed approach has a potential impact on creating a tool for automatically detecting retinal diseases.