A metaheuristic segmentation framework for detection of retinal disorders from fundus images using a hybrid ant colony optimization

Devarajan, Dr.N.; Ramesh, S.; Gomathy, B.

doi:10.1007/s00500-020-04753-7

Cited by 21 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The automation of diagnose comes with implementing machine learning and deep learning algorithms. Current literature gives ample evidence that authors are primarily citing hybrid methods for producing high-precision systems of medical disease detection [34] [35]. Large amounts of citations can be found that are showing the most frequently used machine learning algorithms for detecting eye problems include K-Means, Knearest neighbour (KNN), support vector machine (SVM), ANN or neural networks , decision trees, logistic regression.…”

Section: Reviewmentioning

confidence: 99%

Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Kaur¹,

Singh²

2022

IJACSA

View full text Add to dashboard Cite

For quite some time, the usage of many sources of data (data fusion) and the aggregation of that data have been underappreciated. For the purposes of this study, trials using several medical datasets were conducted, with the results serving as a single aggregated source for identifying eye illnesses. It is proposed in this paper that a diagnostic system that can detect diabetic retinopathy, glaucoma, and cataract can be built as an alternative to current methods. The data fusion and data aggregation techniques used to create this multi-model system made it conceivable. As the name implies, it is a way of compiling data from a large number of legitimate sources. The development of a pipeline of algorithms was accomplished through iterative trials and hyper parameter tweaking. CLAHE (Contrast Level Adaptive Histogram Equalization) approaches, which increase the gradient between picture edges, improve segmentation by raising the contrast between picture edges. The Gabor filter has been shown to be the most effective method of selecting features. The Gabor filter was selected using a hybrid optimization method (LION + Cuckoo), which was developed by the author. For automation, the Support Vector Machine (SVM) radial is the most effective method since it delivers excellent stability and accuracy in terms of accuracy and recall, as well as precision and recall. The discoveries and approaches detailed here provide a more solid foundation for future image-based diagnostics researchers to build on in the future. Eventually, the findings of this study will help to improve healthcare workflows and practices.

show abstract

Section: Reviewmentioning

confidence: 99%

Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Kaur¹,

Singh²

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…In [79], the authors proposed a hybrid optimized neural classifier (HONC) to classify the retinal images and to detect blood vessels. The proposed model used ANN backtracking propagation.…”

Section: Image Segmentation Based On Ant Colony Optimization (Aco)mentioning

confidence: 99%

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

2021

View full text Add to dashboard Cite

Image processing is one example of digital media. It consists of a set of operations to handle an image. Image segmentation is among its main important operations. It involves dividing the image into several parts or regions to extract vital information or identify relevant objects. Many techniques of artificial intelligence, including bio-inspired algorithms, have been used in this regard. This article collected the state-of-the-art studies presenting image-segmentation techniques combined with four bio-inspired algorithms including particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), and artificial bee colonies (ABC). This research work aimed at showing the importance of image segmentation and its combination with these algorithms. This article provides insights on how these algorithms are adapted to image-segmentation combinatorial problems, which assist researchers to start the first hands-on application. It also discusses their setting parameters and the highly used algorithms such as PSO, GA, ACO, and ABC. The article presents new research directions in image segmentation based on bio-inspired algorithms.

show abstract

“…Novel deep learning architectures (Navaneeth and Suchetha 2019 ; Lekha and Suchetha 2017 ; Bhaskar and Manikandan 2019 ; Devarajan et al. 2020 ; Dargan et al. 2019 ) are also used in diverse applications which provide better classification results.…”

Section: Related Workmentioning

confidence: 99%

“…Qiu and Sun (2019) introduced a self-regulated iterative refinement learning (SIRL) strategy that has a pipeline design to build the exhibition of volumetric image classification in macular OCT. discussed the relationship between the geometrical vascular parameters estimated from the fluorescein angiography (FA) and OCT of the eyes with macular edema. Novel deep learning architectures (Navaneeth and Suchetha 2019;Lekha and Suchetha 2017;Bhaskar and Manikandan 2019;Devarajan et al 2020;Dargan et al 2019) are also used in diverse applications which provide better classification results.…”

mentioning

confidence: 99%

Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN

et al. 2021

View full text Add to dashboard Cite

Macular edema (ME) is an essential sort of macular issue caused due to the storing of fluid underneath the macula. Age-related Macular Degeneration (AMD) and diabetic macular edema (DME) are the two customary visual contaminations that can lead to fragmentary or complete vision loss. This paper proposes a deep learning-based predictive algorithm that can be used to detect the presence of a Subretinal hemorrhage. Region Convolutional Neural Network (R-CNN) and faster R-CNN are used to develop the predictive algorithm that can improve the classification accuracy. This method initially detects the presence of Subretinal hemorrhage, and it then segments the Region of Interest (ROI) by a semantic segmentation process. The segmented ROI is applied to a predictive algorithm which is derived from the Fast Region Convolutional Neural Network algorithm, that can categorize the Subretinal hemorrhage as responsive or non-responsive. The dataset, provided by a medical institution, comprised of optical coherence tomography (OCT) images of both pre- and post-treatment images, was used for training the proposed Faster Region Convolutional Neural Network (Faster R-CNN). We also used the Kaggle dataset for performance comparison with the traditional methods that are derived from the convolutional neural network (CNN) algorithm. The evaluation results using the Kaggle dataset and the hospital images provide an average sensitivity, selectivity, and accuracy of 85.3%, 89.64%, and 93.48% respectively. Further, the proposed method provides a time complexity in testing as 2.64s, which is less than the traditional schemes like CNN, R-CNN, and Fast R-CNN.

show abstract

A metaheuristic segmentation framework for detection of retinal disorders from fundus images using a hybrid ant colony optimization

Cited by 21 publications

References 18 publications

Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN

Contact Info

Product

Resources

About