Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G

Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermore, the computational cost of inspecting each pixe...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Medical & biological engineering & computing Jg. 62; H. 8; S. 2571 - 2583
Hauptverfasser: Lim, Wei Xiang, Chen, Zhiyuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024
Springer Nature B.V
Schlagworte:
Algorithms
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
class
Clustering
Computational efficiency
Computer Applications
data collection
Data smoothing
Datasets
Deep Learning
Diabetes
Diabetes mellitus
Diabetic retinopathy
Diabetic Retinopathy - diagnostic imaging
Diabetic Retinopathy - pathology
Exudates
Exudation
Fundus Oculi
Gaussian process
Hemorrhage
Human Physiology
Humans
Hybrid image processing
image analysis
Image classification
Image filters
Image Interpretation, Computer-Assisted - methods
Image processing
Image Processing, Computer-Assisted - methods
Image resolution
Image segmentation
Imaging
Iterative methods
Lesions
Neural Networks, Computer
Normal Distribution
Original Article
Performance evaluation
Photography - methods
Pixels
Radiology
Redundancy
Retinopathy
Segmentation
Smoothing
Diabetic retinopathy
Image processing
Transfer learning
SLIC superpixel segmentation
Image classification
ISSN:0140-0118, 1741-0444, 1741-0444
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermore, the computational cost of inspecting each pixel is expensive because the number of pixels is high even at low resolution. In this work, we propose a hybrid image processing method. Simple Linear Iterative Clustering with Gaussian Filter (SLIC-G) for the purpose of overcoming pixel constraints. The SLIC-G image processing method is divided into two stages: (1) simple linear iterative clustering superpixel segmentation and (2) Gaussian smoothing operation. In such a way, a large number of new transformed datasets are generated and then used for model training. Finally, two performance evaluation metrics that are suitable for imbalanced diabetic retinopathy datasets were used to validate the effectiveness of the proposed SLIC-G. The results indicate that, in comparison to prior published works’ results, the proposed SLIC-G shows better performance on image classification of class imbalanced diabetic retinopathy datasets. This research reveals the importance of image processing and how it influences the performance of deep learning networks. The proposed SLIC-G enhances pre-trained network performance by eliminating the local redundancy of an image, which preserves local structures, but avoids over-segmented, noisy clips. It closes the research gap by introducing the use of superpixel segmentation and Gaussian smoothing operation as image processing methods in diabetic retinopathy–related tasks. Graphical abstract
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0140-0118
1741-0444
1741-0444
DOI:10.1007/s11517-024-03093-0