Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model

Shankar, K.; Sait, Abdul Rahaman Wahab; Gupta, Deepak; Lakshmanaprabu, S.K.; Khanna, Ashish; Pandey, Hari Mohan

doi:10.1016/j.patrec.2020.02.026

Cited by 228 publications

(103 citation statements)

References 23 publications

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“…The alleviated feature set was induced for classification method. Hence the accomplished image, including the best features, designs the precision of classification operation while comparing the classification performance of actual images [24]- [26]. Being a deep structure as well as crucial feed forward network, the Deep Belief Network (DBN) is employed where the instance gets originated from input layer to output layer through maximum number of hidden layers along with additional layers.…”

Section: Image Classificationmentioning

confidence: 99%

Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things

Shobana²,

et al. 2020

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Internet of Medical Things (IoMT) is the collection of medical devices and related applications which link the healthcare IT systems through online computer networks. In the field of diagnosis, medical image classification plays an important role in prediction and early diagnosis of critical diseases. Medical images form an indispensable part of a patient's health record which can be applied to control, handle and treat the diseases. But, classification of images is a challenging task in computer-based diagnostics. In this research article, we have introduced a improved classifier i.e., Optimal Deep Learning (DL) for classification of lung cancer, brain image, and Alzheimer's disease. The researchers proposed the Optimal Feature Selection based Medical Image Classification using DL model by incorporating preprocessing, feature selection and classification. The main goal of the paper is to derive an optimal feature selection model for effective medical image classification. To enhance the performance of the DL classifier, Oppositionbased Crow Search (OCS) algorithm is proposed. The OCS algorithm picks the optimal features from pre-processed images, here Multi-texture, grey level features were selected for the analysis. Finally, the optimal features improved the classification result and increased the accuracy, specificity and sensitivity in the diagnosis of medical images. The proposed results were implemented in MATLAB and compared with existing feature selection models and other classification approaches. The proposed model achieved the maximum performance in terms of accuracy, sensitivity and specificity being 95.22%, 86.45 % and 100% for the applied set of images.

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Section: Image Classificationmentioning

confidence: 99%

Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things

Shobana²,

et al. 2020

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“…Multiple Linear regression is the most common form of linear regression analysis, which is used to signify the relationship between single continuous dependent variable and more than one independent variables. In this paper number of beds re continuous variable which is dependent upon the number of hospitals and number of infected cases of CoV-19 [14][15][16][17][18].…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Indian Health Care System is Ready to Fight Against COVID-19 A Machine Learning Tool for Forecast the Number of Beds

Nagpal

Athavale

Saini

et al. 2020

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)

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Global research team has announced that the health a management system at world level is in fear from CoV-19. Various statistical analysis has been done to check the preparedness to fight against CoV-19. Recent government responses of the different countries are also taken into the consideration while working for CoV-19 handling. Demographic trends are also added to add further content to potential impact of CoV-19 on healthcare services and system. This pandemic has raised a significant challenge to the economy of the different countries. Availability of beds are calculated on Per thousand people in different countries. Few of the countries analysis like Australia is having 2.6 beds per thousand people, while United Kingdom America is having 2.5 beds preparation over 1000 people. Per capita health spending in UK is marginally below the median. Hospital have been urged by government of different countries to postpone their surgeries and other treatments to provide the proper hospitality to cov-19 patients. India is at 145th place among 195 countries in healthcare access and Quality Index (HAQ)[1]. In this paper we have proposed a machine Learning model to predict the number of beds required as Cov-19 cases are increasing. Our Model Predicts the requirement for beds with 95% accuracy and acceptable p-value.

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“…Therefore, with the help of improved MDD classification, it can accomplish more optimized classification accuracy along with best forecasting measure. As a result, different images are used to compute the above work including classification process and result in usual as well as unusual images [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images

2020

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In recent days, progressive rate of diabetic retinopathy (DR) becomes high and it is needed to develop an automated model for effective diagnosis of DR. This paper presents a new deep neural network with moth search optimization (DNN-MSO) algorithm based detection and classification model for DR images. The presented DNN-MSO algorithm involves different processes namely preprocessing, segmentation, feature extraction and classification. Initially, the contrast level of the DR images is improved using contrast limited adaptive histogram equalization model. After that, the preprocessed images are segmented using histogram approach. Then, Inception-ResNet V2 model is applied for feature extraction. Finally, extracted feature vectors are given to the DNN-MSO based classifier model to classify the different stages of DR. An extensive series of experiments were carried out and the results are validated on Messidor DR dataset. The obtained experimental outcome stated the superior characteristics of the DNN-MSO model by attaining a maximum accuracy, sensitivity and specificity of 99.12%, 97.91% and 99.47% respectively.

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Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model

Cited by 228 publications

References 23 publications

Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things

Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things

Indian Health Care System is Ready to Fight Against COVID-19 A Machine Learning Tool for Forecast the Number of Beds

Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images

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