DIO-REGNET: Macular edema detection using Dingo optimized deep Reg network

Macular edema (ME) is a primary cause of blindness and loss of vision in people with visual retinal disorders. Deep learning (DL) algorithms benefit significantly from extensive and diverse datasets during training, but obtaining a sufficient amount of labeled data for macular edema is challenging. An insufficient dataset may result in overfitting, reducing the network’s capability to generalize the diverse cases. An optical coherence tomography (OCT) framework is utilized to solve the problem of diabetic macular edema (DME). Due to the complex nature of this condition and the saturation of healthcare in affluent nations, it is among the main factors that induce blindness. In this paper, a novel DIO-RegNet was introduced for the early recognition of the ME using DL techniques. The input OCT images are pre-processed by a Gaussian adaptive bilateral filter to enhance the image quality. The noise-free images are fed to the Modified DeepLabV3 + to segment the Macular area in retinal images. Then, the segmented Macular region is fed into deep learning-based RegNet for extracting the structural feature. Finally, the Dingo Optimization (DIO) algorithm is applied for the feature selection and classify the cases of macular edema. The proposed DIO-RegNet achieves a detection accuracy of 99.44 % for macular edema. Compared to Dense Net, Alex Net, and ResNet, RegNet achieves an accuracy rate of 96.72 %, 92.89 %, and 97.11 %, respectively. The DIO-RegNet improves overall accuracy by 2.44 %, 5.04 %, and 4.34 % over CNN, faster R-CNN, and VGG-16 CNN, respectively.