An integrated approach for Diabetic Retinopathy exudate segmentation by using Genetic Algorithm and Switching Median Filter

Kaur, Amanjot; Kaur, Prabhpreet

doi:10.1109/icivc.2016.7571284

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…With this model, clinicians can efficiently search for past cases to diagnose existing cases. In [ 26 ], CNNs detected specific diseases in chest radiography images and distributed disease labels. Research [ 27 ] used RNN to describe the context of annotated diseases based on CNN features and patient metadata.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Zhang

Sun

et al. 2021

Journal of Healthcare Engineering

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Computer-aided diagnosis (CAD) has nearly fifty years of history and has assisted many clinicians in the diagnosis. With the development of technology, recently, researches use the deep learning method to get high accuracy results in the CAD system. With CAD, the computer output can be used as a second choice for radiologists and contribute to doctors doing the final right decisions. Chest abnormality detection is a classic detection and classification problem; researchers need to classify common thoracic lung diseases and localize critical findings. For the detection problem, there are two deep learning methods: one-stage method and two-stage method. In our paper, we introduce and analyze some representative model, such as RCNN, SSD, and YOLO series. In order to better solve the problem of chest abnormality detection, we proposed a new model based on YOLOv5 and ResNet50. YOLOv5 is the latest YOLO series, which is more flexible than the one-stage detection algorithms before. The function of YOLOv5 in our paper is to localize the abnormality region. On the other hand, we use ResNet, avoiding gradient explosion problems in deep learning for classification. And we filter the result we got from YOLOv5 and ResNet. If ResNet recognizes that the image is not abnormal, the YOLOv5 detection result is discarded. The dataset is collected via VinBigData’s web-based platform, VinLab. We train our model on the dataset using Pytorch frame and use the mAP, precision, and F1-score as the metrics to evaluate our model’s performance. In the progress of experiments, our method achieves superior performance over the other classical approaches on the same dataset. The experiments show that YOLOv5’s mAP is 0.010, 0.020, 0.023 higher than those of YOLOv5, Fast RCNN, and EfficientDet. In addition, in the dimension of precision, our model also performs better than other models. The precision of our model is 0.512, which is 0.018, 0.027, 0.033 higher than YOLOv5, Fast RCNN, and EfficientDet.

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Section: Related Workmentioning

confidence: 99%

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Zhang

Sun

et al. 2021

Journal of Healthcare Engineering

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“…A technique for exudates identification and segmentation was proposed in [33]. The exudates identification is effective in early detection of diabetic retinopathy, which is effective for saving vision and ensuring the effective treatment.…”

Section: Image Segmentation Based On Genetic Algorithms (Ga)mentioning

confidence: 99%

“…The obtained results were promising. Basic GA P m = 0.01 [31] Science Photo Library Not provided Sorting GA (SGA) Adaptive GA (AGA) Non-dominated sorting GA (NSGA) [33] CHASE database Not provided None [34] Lena, Cameraman, House Iter = [30, 3000] GSA with multi-threshold Berkeley Segmentation dataset N = 30, P m = 0.1 Three GSA variants with multi-level threshold [35] MRI and CT N = 25 and 50 Chan and Vese algorithm P m = 0, 1 and P c = 0.5 [36] MRI brain tumor P c = 0.6, P m = 0.6 None Iter = [300, 450] [37] Grape, Peppers, Brain Iter = 300 2D thresholding methods Light microscopy and others N = 30 [38] 22 benchmark images Iter = [200, 400] Classical thresholding method [39] Plant leaf images None K-means [40] Skin lesions demoscopic images None K-means and γ-K-means [41] Simulated thermal images P c = 0. A set of 12 images Iter = 100, N = 25 6 swarm methods [44] Arbitrary gray images N = 20 None P c = 0.9 P m = 0.05 [45] House N = 400 Otsu's approach PSO is another bio-inspired method used in solving image segmentation.…”

Section: Image Segmentation Based On Genetic Algorithms (Ga)mentioning

confidence: 99%

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

2021

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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.

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“…In [14], the authors applied optic disc identification for exudates and micro-aneurysm extraction-based DR, where they performed a five-class classification: mild, moderate, severe, NPDR, and PDR. To localize exudates, authors used a genetic algorithm [15]. To localize the exudates and other lesions in a fundus image, authors [16] applied the intersection of abnormal thickness in blood vessels.…”

Section: Literature Surveymentioning

confidence: 99%

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

2017

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The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)-and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy-94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR. Keywords Computer-aided diagnosis Á Diabetic retinopathy Á Deep neural network Á AlexNet DNN Á Convolutional neural network Á Gaussian mixture model Á Linear discriminant analysis Á SVM

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An integrated approach for Diabetic Retinopathy exudate segmentation by using Genetic Algorithm and Switching Median Filter

Cited by 10 publications

References 11 publications

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

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