利用深度学习模型和迁移学习技术检测眼疾

Bhavadharini R.M., Kalla Bharath Vardhan, Mandava Nidhish, Surya Kiran C., Dudekula Nahid Shameem, Varanasi Sai Charan
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摘要

导言:糖尿病视网膜病变、白内障和青光眼是主要的眼科疾病,由于其早期无症状,给诊断带来了巨大挑战。这些疾病如果不能在早期发现和诊断,可能会导致严重的视力损伤,甚至导致失明。眼科疾病的早期发现显示了极高的康复率。传统的诊断方法主要依赖眼科领域的专业知识,过程耗时。随着成像技术领域的技术进步,产生了大量医学图像,可用于开发该领域更准确的诊断工具。深度学习(DL)模型在分析医学图像方面发挥着重要作用。深度学习算法可以自动学习眼科图像数据集中显示眼部疾病的特征。然而,训练 DL 模型需要大量的数据和计算资源。为了克服这一问题,我们采用了先进的深度学习算法,并结合了迁移学习技术。利用深度学习的强大功能,我们的目标是开发出能够在医学图像数据中区分不同眼部疾病的复杂模型。目标:方法:对著名的深度学习架构 VGG19、InceptionV3 和 ResNet50 架构与迁移学习进行了评估,并对结果进行了比较。结果:采用迁移学习的 VGG19、InceptionV3 和 ResNet50 架构的准确率分别为 90.33%、89.8% 和 99.94%。VGG19 的精确度、召回率和 F1 分数分别为 79.17%、79.17% 和 78.21%,而 InceptionV3 的精确度、召回率和 F1 分数分别为 82.56%、82.38% 和 82.11%,ResNet50 的精确度、召回率和 F1 分数分别为 96.28%、96.2% 和 96.24%。
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Eye Disease Detection Using Deep Learning Models with Transfer Learning Techniques
INTRODUCTION: Diabetic Retinopathy, Cataract and Glaucoma are the major eye diseases posing significant diagnostic challenges due to their asymptotic nature at their early stages. These diseases if not detected and diagnosed at their early stages may lead to severe visual impairment and even can cause blindness in human beings. Early detection of eye diseases showed an exceptional recovery rate. Traditional diagnostic methods primarily relying on expertise in the field of ophthalmology involve a time-consuming process. With technological advancements in the field of imaging techniques, a large volume of medical images have been created which can be utilized for developing more accurate diagnostic tools in the field. Deep learning (DL) models are playing a significant role in analyzing medical images. DL algorithms can automatically learn the features which indicate eye diseases from eye image datasets. Training DL models, however, requires a significant amount of data and computational resources. To overcome this, we use advanced deep learning algorithms combined with transfer-learning techniques. Leveraging the power of deep learning, we aim to develop sophisticated models that can distinguish different eye diseases in medical image data. OBJECTIVES: To improve the accuracy and efficiency of early detection methods, improve diagnostic precision, and intervene in these challenging ocular conditions in a timely manner. METHODS: The well-known Deep Learning architectures VGG19, InceptionV3 and ResNet50 architectures with transfer learning were evaluated and the results are compared. RESULTS: VGG19, InceptionV3 and ResNet50 architectures with transfer learning achieved 90.33%, 89.8% and 99.94% accuracies, respectively. The precision, recall, and F1 scores for VGG19 were recorded as 79.17%, 79.17%, and 78.21%, while InceptionV3 showed 82.56%, 82.38%, and 82.11% and ResNet50 has 96.28%, 96.2%, and 96.24%. CONCLUSION: The Convolutional Neural Network models VGG19, Inception v3, ResNet50 combined with transfer learning achieve better results than the original Convolutional Neural Network models.
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