Computer Center, Federal Institute of Education, Science and Technology Farroupilha, Alegrete 97555-000, Brazil.
Postgraduate Program in Computing (PPGC), Federal University of Pelotas, Pelotas 96010-610, Brazil.
Sensors (Basel). 2022 Aug 26;22(17):6441. doi: 10.3390/s22176441.
Diabetic Retinopathy is one of the main causes of vision loss, and in its initial stages, it presents with fundus lesions, such as microaneurysms, hard exudates, hemorrhages, and soft exudates. Computational models capable of detecting these lesions can help in the early diagnosis of the disease and prevent the manifestation of more severe forms of lesions, helping in screening and defining the best form of treatment. However, the detection of these lesions through computerized systems is a challenge due to numerous factors, such as the characteristics of size and shape of the lesions, noise and the contrast of images available in the public datasets of Diabetic Retinopathy, the number of labeled examples of these lesions available in the datasets and the difficulty of deep learning algorithms in detecting very small objects in digital images. Thus, to overcome these problems, this work proposes a new approach based on image processing techniques, data augmentation, transfer learning, and deep neural networks to assist in the medical diagnosis of fundus lesions. The proposed approach was trained, adjusted, and tested using the public DDR and IDRiD Diabetic Retinopathy datasets and implemented in the PyTorch framework based on the YOLOv5 model. The proposed approach reached in the DDR dataset an mAP of 0.2630 for the IoU limit of 0.5 and F1-score of 0.3485 in the validation stage, and an mAP of 0.1540 for the IoU limit of 0.5 and F1-score of 0.2521, in the test stage. The results obtained in the experiments demonstrate that the proposed approach presented superior results to works with the same purpose found in the literature.
糖尿病视网膜病变是导致视力丧失的主要原因之一，在其早期阶段，眼底病变表现为微动脉瘤、硬性渗出物、出血和软性渗出物等。能够检测这些病变的计算模型有助于疾病的早期诊断，防止更严重形式的病变表现，有助于筛选和确定最佳的治疗形式。然而，由于多种因素，如病变的大小和形状特征、噪声以及公共糖尿病视网膜病变数据集图像的对比度、数据集中这些病变的标记示例数量以及深度学习算法在数字图像中检测非常小物体的困难等，通过计算机系统检测这些病变是一个挑战。因此，为了克服这些问题，本工作提出了一种基于图像处理技术、数据增强、迁移学习和深度神经网络的新方法，以协助眼底病变的医学诊断。所提出的方法使用公共 DDR 和 IDRiD 糖尿病视网膜病变数据集进行训练、调整和测试，并基于 YOLOv5 模型在 PyTorch 框架中实现。所提出的方法在 DDR 数据集上达到了 0.2630 的 mAP，在验证阶段的 IoU 限制为 0.5，F1 分数为 0.3485，在测试阶段的 IoU 限制为 0.5，F1 分数为 0.2521。实验结果表明，所提出的方法的结果优于文献中具有相同目的的工作。
