Abstract
Diabetic Maculopathy (DM) is considered the most common cause of permanent visual impairment in diabetic patients. The absence of clear pathological symptoms of DM hinders the timely diagnosis and treatment of such a critical condition. Early diagnosis of DM is feasible through eye screening technologies. However, manual inspection of retinography images by eye specialists is a time-consuming routine. Therefore, many deep learning-based computer-aided diagnosis systems have been recently developed for the automatic prognosis of DM in retinal images. Manual tuning of deep learning network's hyperparameters is a common practice in the literature. However, hyperparameter optimization has shown to be promising in improving the performance of deep learning networks in classifying several diseases. This study investigates the impact of using the Bayesian optimization (BO) algorithm on the classification performance of deep learning networks in detecting DM in retinal images. In this research, we propose two new custom Convolutional Neural Network (CNN) models to detect DM in two distinct types of retinal photography; Optical Coherence Tomography (OCT) and fundus retinography datasets. The Bayesian optimization approach is utilized to determine the optimal architectures of the proposed CNNs and optimize their hyperparameters. The findings of this study reveal the effectiveness of using the Bayesian optimization for fine-tuning the model hyperparameters in improving the performance of the proposed CNNs for the classification of diabetic maculopathy in fundus and OCT images. The pre-trained CNN models of AlexNet, VGG16Net, VGG 19Net, GoogleNet, and ResNet-50 are employed to be compared with the proposed CNN-based models. Statistical analyses, based on a one-way analysis of variance (ANOVA) test, receiver operating characteristic (ROC) curve, and histogram, are performed to confirm the performance of the proposed models.