Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering

Rajkumar, Ragavi; Selvarani, A. Grace

doi:10.32604/csse.2022.021909

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…Various literature survey related to the detection of DR has been given in one of our previous work [8]. DR diagnosis with the help of ResNet with Fuzzy Rough C-Means Clustering (FRCM) has been proposed in [9]. The vagueness and uncertainty in the data are removed using a rough set and fuzzy set.…”

Section: Related Workmentioning

confidence: 99%

Genetic Algorithm Based Deep Learning Model Selection for Diabetic Retinopathy Classification

2022

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Background: Diabetic Retinopathy (DR) is a vision-threatening disease as a result of long-term diabetes. DR cannot be cured however it can be controlled if it is detected at an early stage. Hence the early diagnosis of DR is essential to prevent vision loss for diabetic patients. Deep learning models have their footprint in the early diagnosis of disease from medical images. Thus the best deep learning model is selected from various existing deep learning models for DR diagnosis.Method: Genetic Algorithm (GA) is a nature-inspired algorithm based on Darwin's theory of evolution. Here GA is proposed to select the best deep learning model from various existing pretrained deep learning models. Pre-trained models are the initial population of GA. During the GA evolution, the best model is identified or new populations are generated with the help of GA operations.Result: Experiment results show that the GA-based model identification performs better for early diagnosis of DR. It is observed that ResNet50 outperforms various existing pre-trained models VGG16, Inception Net, and GoogleNet.

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Section: Related Workmentioning

confidence: 99%

Genetic Algorithm Based Deep Learning Model Selection for Diabetic Retinopathy Classification

2022

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“…However, different stages of DR were not focused on. To concentrate on this issue, fuzzy rough c-means clustering was employed in [13], with which DR diagnosis accuracy was improved. Another early stage detection method based on optic disc localization was proposed in [14].…”

Section: A Multitask Deep Learning Model Called Multitasking Squeeze ...mentioning

confidence: 99%

Luminosity Normalized Symmetric Deep Convolute Tubular Classifier for Diabetic Retinopathy Detection

Muthusamy

Palani

2022

Preprint

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Diabetic retinopathy (DR) is a common eye disease and a notable starting point of blindness in diabetic patients. Detecting the existence of microaneurysms in the fundus images and the identification of DR in the preliminary stage has always been a considerable question for decades. Systematic screening and appropriate interference are the most efficient mechanism for disease management. The sizeable populations of diabetic patients and their enormous screening requirements have given rise to the computer-aided and automatic diagnosis of DR. The utilizationof Deep Neural Networks in DR diagnosis has also attracted much attention and considerable advancement has been made. However, despite the several advancements that have been made, there remains room for improvement in the sensitivity and specificity of the DR diagnosis. In this work, a novel method called the Luminosity Normalized Symmetric Deep Convolute Tubular Classifier (LN-SDCTC) for DR detection is proposed. The LN-SDCTC method is split into two parts. Initially, with the retinal colorfundus images obtained as input, the Luminosity Normalized Retinal Color Fundus Preprocessing model is applied to produce a noise-minimized enhanced contrast image. Second, the obtained processed image is provided as input to the Symmetric Deep Convolute network. Here, with the aid of the convolutional layer (i.e., the Tubular Neighborhood Window), the average pooling layer (i.e., average magnitude value of tubular neighbors), and the max-pooling layer (i.e., maximum contrast orientation), relevant features are selected. Finally, with the extracted features as input and with the aid of the Multinomial Regression Classification function, the severity of the DR disease is determined. Extensive experimental results in terms of peak signal-to-noise ratio, disease detection time, sensitivity, and specificity reveal that the proposed method of DR detection greatly facilitates the deep learning model and yields better results than various state-of-art methods.

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“…To overcome such draw backs, recent research has been directed toward using deep learning neural networks for diagnosing several diseases [14]. Deep Learning-based computer-aided diagnosis systems have been recognized of their efficiency in automatically identifying the existence of many diseases [15] such as diabetic retinopathy [16], several kinds of cancers [14], and Covid-19 infection [17][18][19]. In computer vision applications, the architecture of CAD systems is specifically based on convolutional neural networks, or convnets, which are utilized for image segmentation [18], classification, object detection and recognition [14,[20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

Atteia¹

2023

Computer Systems Science and Engineering

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Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks (CNNs) to identify the existence of ALL in blood smears. An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images. A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set. The latent features are used to perform image classification using Support Vector Machine (SVM) classifier. The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features. Moreover, the classification performance of the system with various sizes of the latent feature set is evaluated. The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.

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Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering

Cited by 9 publications

References 26 publications

Genetic Algorithm Based Deep Learning Model Selection for Diabetic Retinopathy Classification

Genetic Algorithm Based Deep Learning Model Selection for Diabetic Retinopathy Classification

Luminosity Normalized Symmetric Deep Convolute Tubular Classifier for Diabetic Retinopathy Detection

Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

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