LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

Abstract

Diabetes problems can lead to an eye disease called Diabetic Retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated early, DR becomes a significant reason for blindness. To identify the DR and determine the stages, medical tests are very labor-intensive, expensive, and timeconsuming. To address the issue, a hybrid deep and machine learning techniquebased autonomous diagnostic system is provided in this paper. Our proposal is based on lesion segmentation of the fundus images based on the LuNet network. Then a Refined Attention Pyramid Network (RAPNet) is used for extracting global and local features. To increase the performance of the classifier, the unique features are selected from the extracted feature set using Aquila Optimizer (AO) algorithm. Finally, the LightGBM model is applied to classify the input image based on the severity. Several investigations have been done to analyze the performance of the proposed framework on three publically available datasets (MESSIDOR, APTOS, and IDRiD) using several performance metrics such as accuracy, precision, recall, and f1-score. The proposed classifier achieves 99.29%, 99.35%, and 99.31% accuracy for these three datasets respectively. The outcomes of the experiments demonstrate that the suggested technique is effective for disease identification and reliable DR grading.

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

“…Addressing this constraint, U-Net enhances image feature fusion by introducing an encoder-decoder structure based on FCN. The encoder module of U-Net utilizes convolution and down-sampling to extract shallow features from the image [88][89][90][91]. Simultaneously, the decoder module employs deconvolution and up-sampling to capture deep image features.…”

Reconstruction and Intelligent Evaluation of Three-Dimensional Texture of Stone Matrix Asphalt-13 Pavement for Skid Resistance

“…The most commonly used are integrated models of decision trees, such as the bagging model: random forest (RF), and boosting model: gradient-boosting machine (GBM), etc. Compared with the boosting model, the bagging model cannot improve model deviation or significantly im-prove performance; the processing of unbalanced datasets is limited [21][22][23][24]. This paper chooses the gradient-boosting machine model in the boosting model, there are mainly gradient-boosting decision trees (GBDTs), extreme gradient-boosting (XGBoost), and light gradient-boosting machines (LightGBMs).…”