Analysis of Convolutional Neural Network for Fundus Image Segmentation

Shirokanev, A. S.; Ilyasova, Nataly; Демин, Н. С.

doi:10.1088/1742-6596/1438/1/012016

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…The resultant values are compared with state-of-art works such as DOFE [ 41 ], QUINCUNX [ 42 ], CNN [ 43 ], MT approaches. Figure 6 depicts the comparative analyses of the proposed MTRO approach with the other state-of-art works such as DOFE [ 41 ], QUINCUNX [ 42 ], CNN [ 43 ], and MT approaches in terms of positive predictive values. From Fig.…”

Section: Resultsmentioning

confidence: 99%

Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images

Vinayaki

Kalaiselvi

2022

Neural Process Lett

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One of the most common complications of diabetes mellitus is diabetic retinopathy (DR), which produces lesions on the retina. A novel framework for DR detection and classification was proposed in this study. The proposed work includes four stages: pre-processing, segmentation, feature extraction, and classification. Initially, the image pre-processing is performed and after that, the Multi threshold-based Remora Optimization (MTRO) algorithm performs the vessel segmentation. The feature extraction and classification process are done by using a Region-based Convolution Neural Network (R-CNN) with Wild Geese Algorithm (WGA). Finally, the proposed R-CNN with WGA effectively classifies the different stages of DR including Non-DR, Proliferative DR, Severe, Moderate DR, Mild DR. The experimental images were collected from the DRIVE database, and the proposed framework exhibited superior DR detection performance. Compared to other existing methods like fully convolutional deep neural network (FCDNN), genetic-search feature selection (GSFS), Convolutional Neural Networks (CNN), and deep learning (DL) techniques, the proposed R-CNN with WGA provided 95.42% accuracy, 93.10% specificity, 93.20% sensitivity, and 98.28% F-score results.

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Section: Resultsmentioning

confidence: 99%

Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images

Vinayaki

Kalaiselvi

2022

Neural Process Lett

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“…Illumination can be distorted in three ways: darkness, brightness, and uneven illumination [55]. To address this, RGB color channels can be transformed into the YUV channels where the Y channel (Fig 9D ) represents illumination components [47], [55], [56]. In our study, we converted the images of NLMD output into YUV color space format and separated the Y Channel.…”

Section: ) Image Enhancement (Clahe)mentioning

confidence: 99%

A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images

et al. 2023

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Diabetic retinopathy (DR) is a common complication of diabetes mellitus, and retinal blood vessel damage can lead to vision loss and blindness if not recognized at an early stage. Manual DR detection using large fundus image data is time-consuming and error-prone. An effective automatic DR detection system can be significantly faster and potentially more accurate. This study aims to classify fundus images into five DR classes, using deep learning methods, with the highest possible accuracy and the lowest possible computational time. Three distinct DR datasets, APTOS, Messidor2, and IDRiD, are merged, resulting in 5,819 raw images. Before training the model, various image preprocessing techniques are applied to remove artifacts and noise from the images and improve their quality. Three augmentation techniques: geometric, photometric, and elastic deformation, are used to create a balanced dataset. A shallow convolutional neural network (CNN) is developed using three blocks of convolutional layers and maxpool layers with a categorical cross-entropy loss function, Adam optimizer, 0.0001 learning rate, and 64 batch size as a base model, and this is also employed to determine the best data augmentation method for further processing. A study to optimize the performance is then conducted by changing different components and hyperparameters of the base model, resulting in our proposed RetNet-10 model. Six cutting-edge models are employed for comparison. Our proposed RetNet-10 model performed the best, with a testing accuracy of 98.65%. MobileNetV2, VGG16, Xception, VGG19, InceptionV3 and ResNet50 achieved testing accuracies of 91.42%, 90.16%,89.57%, 88.21%, 87.68% and 87.23%, respectively. The model is also trained with several k values to assess its robustness. After image processing and data augmentation, using the combined dataset, and fine-tuning the base model, our proposed RetNet-10 model outperformed other automated methods for DR diagnosis.

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“…In recent years, hyperspectral imaging technology has been developed increasingly mature. With the features of multiple spectral channels, high spectral resolution, strong band continuity, and "map unity, " hyperspectral imaging technology is widely used in remote sensing (Nalepa et al, 2020;Tu et al, 2020), agriculture (Jiang et al, 2016;Moliner and Romero, 2020;Zhang et al, 2020), biomedicine (Shirokanev et al, 2020;Trajanovski et al, 2020), and other fields. The data acquired by hyperspectral imaging techniques are called hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing

Tan

Zhang

et al. 2021

Front. Plant Sci.

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This paper focuses on image segmentation, image correction and spatial-spectral dimensional denoising of images in hyperspectral image preprocessing to improve the classification accuracy of hyperspectral images. Firstly, the images were filtered and segmented by using spectral angle and principal component analysis, and the segmented results are intersected and then used to mask the hyperspectral images. Hyperspectral images with a excellent segmentation result was obtained. Secondly, the standard reflectance plates with reflectance of 2 and 98% were used as a priori spectral information for image correction of samples with known true spectral information. The mean square error between the corrected and calibrated spectra is less than 0.0001. Comparing with the black-and-white correction method, the classification model constructed based on this method has higher classification accuracy. Finally, the convolution kernel of the one-dimensional Savitzky-Golay (SG) filter was extended into a two-dimensional convolution kernel to perform joint spatial-spectral dimensional filtering (TSG) on the hyperspectral images. The SG filter (m = 7,n = 3) and TSG filter (m = 3,n = 4) were applied to the hyperspectral image of Pavia University and the quality of the hyperspectral image was evaluated. It was found that the TSG filter retained most of the original features while the noise information of the filtered hyperspectral image was less. The hyperspectral images of sample 1–1 and sample 1–2 were processed by the image segmentation and image correction methods proposed in this paper. Then the classification models based on SG filtering and TSG filtering hyperspectral images were constructed, respectively. The results showed that the TSG filter-based model had higher classification accuracy and the classification accuracy is more than 98%.

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Analysis of Convolutional Neural Network for Fundus Image Segmentation

Cited by 7 publications

References 6 publications

Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images

Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images

A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images

Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing

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