The Use of U-Net Lite and Extreme Gradient Boost (XGB) for Glaucoma Detection

Afolabi, Oluwatobi J.; Mabuza-Hocquet, Gugulethu; Nelwamondo, Fulufhelo V.; Paul, Babu Sena

doi:10.1109/access.2021.3068204

Cited by 22 publications

(9 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, U-Net can automatically acquire deep semantic features through the multi-dimensional feature learning of convolutional neural networks, effectively reducing the classification process's noise. So, in recent years, U-Net model was successfully used for image segmentation [38], [39]. The LiDAR and photogrammetric point clouds were also segmented based on the polygons from U-Net model segmentation results.…”

Section: Discussionmentioning

confidence: 99%

3D Urban Buildings Extraction Based on Airborne LiDAR and Photogrammetric Point Cloud Fusion According to U-Net Deep Learning Model Segmentation

Zhang

Wang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

The LiDAR and photogrammetric point clouds fusion procedure for building extraction according to U-Net deep learning model segmentation is provided and tested. Firstly, an initial geolocalization process is performed for photogrammetric point clouds generated using structure-from-motion and dense-matching methods. Then, point cloud segmentation is carried out based on U-Net deep learning model. The precision of the U-Net model for buildings extraction reachs 87%, with F-score of 0.89 and IoU of 0.80. It is shown that the U-Net method is effective for high-resolution image extraction. The detailed information can accurately be identified and extracted, such as vegetation located between buildings and roads. After segmentation, each chunk of the LiDAR and photogrammetric point clouds are finely registered and merged based on the iterative closest point algorithm. Finally, the fused point clouds are obtained. It shows that the structure and shape of the buildings could be delineated from the fused point clouds when both enough ground points and a higher point density are available. Furthermore, color information improves both visualization effect and properties identification. The experiments are conducted to extract individual buildings from three types of point clouds in three plots. A DoN (Difference of Normals) approach is used to isolate 3D buildings from other objects in densely built-up areas. It shows that most building extraction results have a Precision > 0.9 and favorable Recall and F-score values. Although the LiDAR extraction results have some advantages over the photogrammetric and fused ones in terms of Precision, the Recall and F-score results appear best for the fused point clouds. It shows that the fused data contains a high point density and RGB color information and could improve the building extraction.

show abstract

Section: Discussionmentioning

confidence: 99%

3D Urban Buildings Extraction Based on Airborne LiDAR and Photogrammetric Point Cloud Fusion According to U-Net Deep Learning Model Segmentation

Zhang

Wang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…XGBoost classifier. Afolabi and coworkers proposed an XG-Boost classifier resulting in an accuracy of 88.3% and AUC of 93.6% via 5-fold cross-validation ( 3 ).…”

Section: Machine Learning/statistical Modeling-based Ai Classifiers A...mentioning

confidence: 99%

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

Coan¹,

Williams²,

Venkatesh³

et al. 2023

Survey of Ophthalmology

View full text Add to dashboard Cite

“…Finally, it can be implemented through various image processing tools and deep learning‐based CNN models. Afolabi et al 84 compared the performance of the U‐Net model on Drives, Drishti‐GS, Rim‐One V2 and the Rim‐One V3 datasets using extreme gradient boost algorithm for glaucoma detection. The proposed method produced better results than the CDR threshold values approach.…”

Section: Image Processing Techniques Applied In Selected Research Art...mentioning

confidence: 99%

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Lalithadevi

Krishnaveni²

2022

Concurrency and Computation

View full text Add to dashboard Cite

Diabetes mellitus is a chronic disorder disease in which a person's body fails to adhere insulin produced by their pancreas or unable to segregate enough insulin due to harmonic imbalance. Diabetic people are suffering from eye disorders like diabetic retinopathy (DR), glaucoma and various diseases such as neuropathy, nephropathy, cardiomyopathy over long intervals. One of the most prevalent diabetic consequence is DR. Detecting the morphological variations in retina is difficult and requires an effective automated detection system. DR can be predicted in earlier stage using tremendous development of deep learning models and image processing techniques. Recently, many research articles have been published in DR diagnosis system. This article shows a comprehensive review of automated diagnostic methods for DR detection and other related eye disorders from several points: Causes for DR, publicly available datasets, image preprocessing, segmentation of various DR lesions, feature optimization, various deep learning models, and open research challenges. The study offers a thorough overview of DR detection techniques, which delivers valuable information for researchers, medical professionals, and DR affected patients.

show abstract

The Use of U-Net Lite and Extreme Gradient Boost (XGB) for Glaucoma Detection

Cited by 22 publications

References 64 publications

3D Urban Buildings Extraction Based on Airborne LiDAR and Photogrammetric Point Cloud Fusion According to U-Net Deep Learning Model Segmentation

3D Urban Buildings Extraction Based on Airborne LiDAR and Photogrammetric Point Cloud Fusion According to U-Net Deep Learning Model Segmentation

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Contact Info

Product

Resources

About