Artificial intelligence has the potential to revolutionize disease diagnosis, classification, and identification. However, the implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. This study presents a diagnostic tool based on a deep-learning framework for four-class classification of ocular diseases by automatically detecting diabetic macular edema, drusen, choroidal neovascularization, and normal images in optical coherence tomography (OCT) scans of the human eye. The proposed framework utilizes OCT images of the retina and analyses using three different convolution neural network (CNN) models (five, seven, and nine layers) to identify the various retinal layers extracting useful information, observe any new deviations, and predict the multiple eye deformities. The framework utilizes OCT images of the retina, which are preprocessed and processed for noise removal, contrast enhancements, contour-based edge, and detection of retinal layer extraction. This image dataset is analyzed using three different CNN models (of five, seven, and nine layers) to identify the four ocular pathologies. Results obtained from the experimental testing confirm that our model has excellently performed with 0.965 classification accuracy, 0.960 sensitivity, and 0.986 specificities compared with the manual ophthalmological diagnosis.

人工智能具有彻底改变疾病诊断，分类和识别的潜力。然而，用于医学成像的临床决策支持算法的实施面临着可靠性和可解释性方面的挑战。这项研究提出了一种基于深度学习框架的诊断工具，可通过在人眼的光学相干断层扫描（OCT）扫描中自动检测糖尿病性黄斑水肿，玻璃膜疣，脉络膜新生血管形成和正常图像，对眼部疾病进行四类分类。提出的框架利用视网膜的OCT图像，并使用三种不同的卷积神经网络（CNN）模型（五层，七层和九层）进行分析，以识别提取有用信息的各个视网膜层，观察任何新的偏差并预测多眼畸形。该框架利用视网膜的OCT图像进行预处理和处理，以去除噪声，增强对比度，基于轮廓的边缘以及检测视网膜层提取。使用三种不同的CNN模型（五层，七层和九层）分析此图像数据集，以识别四种眼部病理。从实验测试获得的结果证实，与人工眼科诊断相比，我们的模型具有0.965的分类精度，0.960的灵敏度和0.986的特异性，表现出色。和9层）来识别四种眼部疾病。从实验测试获得的结果证实，与人工眼科诊断相比，我们的模型具有0.965的分类精度，0.960的灵敏度和0.986的特异性，表现出色。和9层）来识别四种眼部疾病。从实验测试获得的结果证实，与人工眼科诊断相比，我们的模型具有0.965的分类精度，0.960的灵敏度和0.986的特异性，表现出色。