Segmentation of Hard exudates for the detection of Diabetic Retinopathy with RNN based sematic features using fundus images

Sivapriya, G.; Praveen, V.; Gowri, P.; Saranya, S; Sweetha, S.; Shekar, Kukunoor

doi:10.1016/j.matpr.2022.05.189

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…For instance, figure 5 shows the three rows, where input image in first row, ground truth image in second row and finally, segmented images using proposed model in third row. [22,27], RNN [39], CNN [41], GAN [42], YOLO [26,27], CSO [31] and SSA [31] are all tested with these two datasets and results are mentioned in the following tables.…”

Section: Segmentation Analysismentioning

confidence: 99%

YOLO Based Deep Learning Model for Segmenting the Color Images

Rasi¹,

AntoBennet

Renjith

et al. 2023

IJEER

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The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation. This study seeks to build a light-weight for object detector that uses a depth and colour image from a publically available dataset to identify objects in a scene. It's likely to output in the depth way by expanding the YOLO network's network architecture. Using Taylor based Cat Salp Swarm algorithm (TCSSA), the weight of the suggested model is modified to improve the accuracy of region extraction findings. It is possible to test the detector's efficacy by comparing it to various datasets. Testing showed that the suggested model is capable of segmenting input into multiple metrics using bounding boxes. The results shows that the proposed model achieved 0.20 of Global Consistency Error (GCE) and 1.85 of Variation of Information (VOI) on BSDS500 dataset, where existing techniques achieved nearly 1.96 to 1.86 of VOI and 0.25 to 0.22 of GCE for the same dataset.

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Section: Segmentation Analysismentioning

confidence: 99%

YOLO Based Deep Learning Model for Segmenting the Color Images

Rasi¹,

AntoBennet

Renjith

et al. 2023

IJEER

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“…It uses weighted CLAHE, Gaussian blur and Ben Grahams fraction maxpooling for preprocessing. Sivapriya et al [75] have proposed a Recurrent Neural Network (RNN) to identify hard EXs for the detection of DR. The model uses limited data of 400 images from the MESSIDOR dataset and performs various preprocessing operations on them without any significant changes in the architecture of the RNN.…”

Section: The Proposed Model Identifies High Bias and High Variance Du...mentioning

confidence: 99%

Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC)

Das

Biswas

Bandyopadhyay

2022

Multimed Tools Appl

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Diabetic Retinopathy (DR) is caused as a result of Diabetes Mellitus which causes development of various retinal abrasions in the human retina. These lesions cause hindrance in vision and in severe cases, DR can lead to blindness. DR is observed amongst 80% of patients who have been diagnosed from prolonged diabetes for a period of 10–15 years. The manual process of periodic DR diagnosis and detection for necessary treatment, is time consuming and unreliable due to unavailability of resources and expert opinion. Therefore, computerized diagnostic systems which use Deep Learning (DL) Convolutional Neural Network (CNN) architectures, are proposed to learn DR patterns from fundus images and identify the severity of the disease. This paper proposes a comprehensive model using 26 state-of-the-art DL networks to assess and evaluate their performance, and which contribute for deep feature extraction and image classification of DR fundus images. In the proposed model, ResNet50 has shown highest overfitting in comparison to Inception V3, which has shown lowest overfitting when trained using the Kaggle’s EyePACS fundus image dataset. EfficientNetB4 is the most optimal, efficient and reliable DL algorithm in detection of DR, followed by InceptionResNetV2, NasNetLarge and DenseNet169. EfficientNetB4 has achieved a training accuracy of 99.37% and the highest validation accuracy of 79.11%. DenseNet201 has achieved the highest training accuracy of 99.58% and a validation accuracy of 76.80% which is less than the top-4 best performing models.

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“…Si hacemos la comparativa de los resultados obtenido por [26] indica que usando árbol de decisión tiene una precisión de 87.5% frente a la presente investigación usando una estrategia de datos aplicando el algoritmo Sobel, orientación, medfild2 y a esto redimensionarlo para obtener su representativo en una dimensión pequeña tal como se aprecia en la Fig 3 . A estos nuevos datos vienen a ser los patrones de entrada a la red neuronal SOM obteniendo un 93.7% de precisión. Teniendo en cuenta que los casos usan imágenes que tienen retinopatía diabética, tal como la investigación de [23] que tuvo un 96.11% de certeza implicando una diferencia mayor de 2.41% de precisión frente a la presente investigación, por tanto se requiere unos ajustes para alcanzar o superar esta precisión.…”

Section: Conclusionesunclassified