Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images

Malliga, S.; Kumar, M. Sandeep; Sathishkumar, V E; Jayagopal, Prabhu; Karthick, Alagar; Ganesh, S. Sankar; Meem, Mahseena Akter

doi:10.1155/2022/8014979

Cited by 39 publications

(32 citation statements)

References 47 publications

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“…As a result, numerous image-processing methods for detecting asphalt pavement cracks where fissures have been formed. Image thresholding algorithmbased road crack identification models by Subramanian et al [19], Alam et al [20], and Kogilavani et al [21] have all provided solutions. Furthermore, many scholars [22][23][24] have established models based on edge detection algorithms.…”

Section: Existing Methods and Review Of Literaturementioning

confidence: 99%

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IoT‐Based Intelligent System for Internal Crack Detection in Building Blocks

Babu¹,

Kumar

Jayagopal

et al. 2022

Journal of Nanomaterials

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Cracks that are detected in concrete structures represent significant damage, and they can lead to a detrimental effect on the structure’s durability. Their identification in a timely manner can help ensure structural safety and guide in-depth maintenance operation. Automatic detection of such cracks has been proposed using internal crack detection utilizing ultrasonic sensors in concrete. Cracks within the concrete can be detected using ultrasonic sensors. In this investigation, we introduced an intelligent method that is aimed at developing a crack detection scheme using ultrasonic sensors. These ultrasonic sensors are used for the detection of cracks in buildings which cannot be seen with our naked eyes; they are capable of alerting authorities via SMS message and providing the cracks’ location via GSM and GPS modules. To monitor internal cracks in the concrete cubes and cylinders, the ultrasonic sensors can be fixed at the centre of the cube which will be used for interval crack monitoring based on crack detection technology. The grade of concrete used for testing is M25, and it is well mixed with the ingredients of cement, fine aggregate, coarse aggregate, and water. The concrete is placed in the cube moulds having the dimensions 150 mm × 150 mm × 150 mm . The cylinders used in the case of the experimental analysis are of the dimensions of 150 mm diameter and 300 mm height. These specimens are cast and kept in the curing tank for 28 days to attain the maximum strength. After completion of the curing period, the specimens were taken out from the tank and weighed. After this weighing process, the cubes and cylinders are about 8.884 kg and 13.399 kg, respectively. The information about the cracks can be displayed on the LCD, and also, the transmitted short message about the cracks can be exchanged between the devices using IoT.

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Section: Existing Methods and Review Of Literaturementioning

confidence: 99%

“…The sensors are used to detect the presence of cracks and display the message on an LCD display if the cracks are detected [16][17][18]. As a result, this proposed method reduces the automobile accidents and saves lives and also reduces the economic losses [19]. The block diagram of the proposed method is shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

IoT‐Based Intelligent System for Internal Crack Detection in Building Blocks

Babu¹,

Kumar

Jayagopal

et al. 2022

Journal of Nanomaterials

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“…It can reduce error rate and find the connection weights as well as bias values. A particle swarm optimization [ 2 ] is matched with the backpropagation approach for training the dataset. It produces maximum accuracy in prediction.…”

Section: Related Workmentioning

confidence: 99%

“…The artificial neural network (ANN) can do intellectual responsibilities like the human brain. A popular trustworthy classification method from the NN is the multilayer backpropagation network [ 2 ]. And a radial basis function [ 3 ] is a dominant neural approach that uses radial basis procedures.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing Breast Cancer Based on the Adaptive Neuro-Fuzzy Inference System

Chidambaram

Ganesh

Karthick

et al. 2022

Computational and Mathematical Methods in Medicine

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In this work, a novel hybrid neuro-fuzzy classifier (HNFC) technique is proposed for producing more accuracy in input data classification. The inputs are fuzzified using a generalized membership function. The fuzzification matrix helps to create connectivity between input pattern and degree of membership to various classes in the dataset. According to that, the classification process is performed for the input data. This novel method is applied for ten number of benchmark datasets. During preprocessing, the missing data is replaced with the mean value. Then, the statistical correlation is applied for selecting the important features from the dataset. After applying a data transformation technique, the values normalized. Initially, fuzzy logic has been applied for the input dataset; then, the neural network is applied to measure the performance. The result of the proposed method is evaluated with supervised classification techniques such as radial basis function neural network (RBFNN) and adaptive neuro-fuzzy inference system (ANFIS). Classifier performance is evaluated by measures like accuracy and error rate. From the investigation, the proposed approach provided 86.2% of classification accuracy for the breast cancer dataset compared to other two approaches.

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“…Open-access CT images and clinical symptoms from around the world are needed to help in the fight against COVID-19. Because we are working with CT slides as our input images, the problem with detecting COVID-19 is one of the image classifications [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis

Sangeetha

Kumar

Deeba

et al. 2022

Computational and Mathematical Methods in Medicine

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As a result of the COVID-19 outbreak, which has put the world in an unprecedented predicament, thousands of people have died. Data from structured and unstructured sources are combined to create user-friendly platforms for clinicians and researchers in an integrated bioinformatics approach. The diagnosis and treatment of COVID-19 disease can be accelerated using AI-based platforms. In the battle against the virus, however, researchers and decision-makers must contend with an ever-increasing volume of data, referred to as “big data.” VGG19 and ResNet152V2 pretrained deep learning architectures were used in this study. With these datasets, we could train and fine-tune our model on lung ultrasound frames from healthy people as well as from patients with COVID-19 and pneumonia. In two separate experiments, we evaluated two different classes of predictive models: one against pneumonia and the other against non-COVID-19. COVID-19 can be detected and diagnosed accurately and efficiently using these models, according to the findings. Therefore, the use of these inexpensive and affordable deep learning methods should be considered as a reliable method for the diagnosis of COVID-19.

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Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images

Cited by 39 publications

References 47 publications

IoT‐Based Intelligent System for Internal Crack Detection in Building Blocks

IoT‐Based Intelligent System for Internal Crack Detection in Building Blocks

Diagnosing Breast Cancer Based on the Adaptive Neuro-Fuzzy Inference System

An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis

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